{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPS_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Jul 2016, 07:29:41
{txt}
{com}. 
. use "/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/noaasammy.dta"
{txt}
{com}. 
. ********************************************************
. ///* DEMOGRAPHIC VARIABLES AND POLITICAL ORIENTATION*///
> ********************************************************
. ** generate id variable 
. gen id = _n
{txt}
{com}. 
. /*RACE*/
. /*white = 1 non-white = 0 */
. /*Lose 37 cases */
. rename q119fin1 race
{txt}
{com}. recode race 1=0 2=1 3=0 4=0 5=0 100=0 101=0 102=0 103=0
{txt}(race: 1056 changes made)

{com}. label drop q119fin1
{txt}
{com}. label define q119fin1 0 "non-white" 1 "white"
{txt}
{com}. tab race

       {txt}race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  non-white {c |}{res}        168       15.91       15.91
{txt}      white {c |}{res}        888       84.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,056      100.00
{txt}
{com}. 
. /*EDUCATION*/
. /*Less than high school = 0 Post-Graduate = 1*/
. rename q116 education
{txt}
{com}. recode education 3=2 4=3 5=4 6=5
{txt}(education: 838 changes made)

{com}. label drop q116
{txt}
{com}. label define q116 1 "some high school"2 "high school/vocational" 3 "some college"  4 "college" 5 "post-graduate" 
{txt}
{com}. codebook education 

{txt}{hline}
{res}education{right:education}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q116}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}12{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}     22{col 33}       1{col 43}{txt}some high school
{col 24}{res}    241{col 33}       2{col 43}{txt}high school/vocational
{col 24}{res}    303{col 33}       3{col 43}{txt}some college
{col 24}{res}    331{col 33}       4{col 43}{txt}college
{col 24}{res}    184{col 33}       5{col 43}{txt}post-graduate
{col 24}{res}     12{col 33}       .{col 43}
{txt}
{com}. 
. /*INCOME*/
. /*Lose 244 cases*/
. rename q122 income
{txt}
{com}. 
. /*AGE*/
. /*Ranges from 18 to 90*/
. /*Lose 32 cases*/
. rename q117 age
{txt}
{com}. 
. /*RELIGIOUS ATTENDANCE*/
. /* Attendend = 1 Not attend = 0*/
. /* lose 16 cases*/
. rename q124 attendance
{txt}
{com}. recode attendance 2=0
{txt}(attendance: 0 changes made)

{com}. 
. /*IDEOLOGY*/
. /* Lose 134 cases*/
. rename q118 ideo
{txt}
{com}. 
. /*PARTISANSHIP*/
. /* Lose 46 cases*/
. rename q114 pid
{txt}
{com}. 
. ** recode democrat = 1 republican = -1
. recode pid 2 = 0 3 = 1 1 = 2 8 = 1 9 = 1 
{txt}(pid: 1093 changes made)

{com}. label drop q114
{txt}
{com}. label define q114 0 "republican" 1 "independent/else" 2 "democrat"  
{txt}
{com}. codebook pid 

{txt}{hline}
{res}pid{right:suppose you were in the voting booth and you came across an office for which two}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q114}

{col 18}range:  [{res}0{txt},{res}2{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}0{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    256{col 33}       0{col 43}{txt}republican
{col 24}{res}    480{col 33}       1{col 43}{txt}independent/else
{col 24}{res}    357{col 33}       2{col 43}{txt}democrat

{com}. 
. /*EFFICACY*/
. /*Higher values are associated with lower efficacy*/
. /*Lose 102 cases*/
. cor q71 q73 q74
{txt}(obs=991)

             {c |}      q71      q73      q74
{hline 13}{c +}{hline 27}
         q71 {c |}{res}   1.0000
         {txt}q73 {c |}{res}   0.3621   1.0000
         {txt}q74 {c |}{res}   0.4882   0.2457   1.0000

{txt}
{com}. factor q71 q73 q74
{txt}(obs=991)

Factor analysis/correlation{col 52}Number of obs    = {res}     991
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.98296      1.04220            1.4417       1.4417
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.05924      0.18268           -0.0869       1.3548
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.24192            .           -0.3548       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  415.39{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q71}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6594}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5652}}}{space 1}
{space 4}{space 0}{ralign 12:q73}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4526}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7952}}}{space 1}
{space 4}{space 0}{ralign 12:q74}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5859}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6567}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q71 q73 q74, detail gen (efficacy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .1693972
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6338

{txt}Interitem covariances (obs=pairwise, see below)

        q71     q73     q74
q71  {res}0.5078
{txt}q73  {res}0.1624  0.4029
{txt}q74  {res}0.2380  0.1055  0.4767

{txt}Pairwise number of observations

      q71   q73   q74
q71  {res}1073
{txt}q73  {res}1023  1035
{txt}q74  {res}1033   999  1045
{txt}
{com}. 
. /*RISK PERCEPTIONS*/
. /*Higher values are associated with higher risk perception */ 
. recode q101 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q101: 1037 changes made)

{com}. recode q102 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q102: 1014 changes made)

{com}. recode q103 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q103: 1031 changes made)

{com}. factor q101 q102 q103 
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.77400      1.89251            1.1834       1.1834
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.11851      0.03796           -0.0791       1.1044
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.15648            .           -0.1044       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1191.31{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7924}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3720}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7273}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4710}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7855}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3829}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q101 q102 q103, detail gen (risk)  

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .040741
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8428

{txt}Interitem covariances (obs=pairwise, see below)

        q101    q102    q103
q101  {res}0.0722
{txt}q102  {res}0.0407  0.0592
{txt}q103  {res}0.0450  0.0364  0.0591

{txt}Pairwise number of observations

      q101  q102  q103
q101  {res}1037
{txt}q102  {res} 998  1014
{txt}q103  {res}1007   993  1031
{txt}
{com}. 
. /*NETWORK INTEREST*/
. /*Higher values associated with greater network interest*/
. recode q81 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q81: 1087 changes made)

{com}. recode q82 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q82: 1087 changes made)

{com}. factor q81 q82 q83 q85
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.81814      1.53358            1.0636       1.0636
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.28456      0.47109            0.1665       1.2301
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.18653      0.02025           -0.1091       1.1210
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.20678            .           -0.1210       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1382.63{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7265}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2610}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4041}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7721}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2041}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3622}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6111}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2818}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5472}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5664}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3089}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5838}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q81 q82 q83 q85, detail gen (network)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0729956
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7462

{txt}Interitem covariances (obs=pairwise, see below)

        q81     q82     q83     q85
q81  {res}0.1045
{txt}q82  {res}0.0727  0.1010
{txt}q83  {res}0.0594  0.0667  0.2449
{txt}q85  {res}0.0501  0.0593  0.1296  0.2390

{txt}Pairwise number of observations

      q81   q82   q83   q85
q81  {res}1087
{txt}q82  {res}1086  1087
{txt}q83  {res}1086  1086  1089
{txt}q85  {res}1081  1081  1084  1084
{txt}
{com}. 
. 
. ** create ideological strength 
. gen strength_ideo = 0 
{txt}
{com}. recode strength_ideo 0 = 3 if ideo == 1
{txt}(strength_ideo: 58 changes made)

{com}. recode strength_ideo 0 = 3 if ideo == 7
{txt}(strength_ideo: 119 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 6
{txt}(strength_ideo: 180 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 2
{txt}(strength_ideo: 179 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 3
{txt}(strength_ideo: 108 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 5
{txt}(strength_ideo: 145 changes made)

{com}. 
. 
. ************************************
. ///* INDICATORS OF INFORMATION *///
> ************************************
. /* SCIENTIFIC INFORMATION 1 */
. /* 1 correct, 0 wrong */
. /* Lose 13 cases */
. cor q12 q13 q14 q15
{txt}(obs=1080)

             {c |}      q12      q13      q14      q15
{hline 13}{c +}{hline 36}
         q12 {c |}{res}   1.0000
         {txt}q13 {c |}{res}   0.0521   1.0000
         {txt}q14 {c |}{res}   0.2260   0.0802   1.0000
         {txt}q15 {c |}{res}   0.2196   0.0005   0.1004   1.0000

{txt}
{com}. factor q12 q13 q14 q15 
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.45391      0.42155            2.1711       2.1711
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.03236      0.11624            0.1548       2.3259
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08388      0.10945           -0.4012       1.9247
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.19332            .           -0.9247       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  121.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q12}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4447}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0189}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8019}}}{space 1}
{space 4}{space 0}{ralign 12:q13}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1176}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1449}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9652}}}{space 1}
{space 4}{space 0}{ralign 12:q14}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3625}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0571}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8653}}}{space 1}
{space 4}{space 0}{ralign 12:q15}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3330}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0881}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8814}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q12 q13 q14 q15, detail gen (sci_obknowledge)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240184
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.3552

{txt}Interitem covariances (obs=pairwise, see below)

        q12     q13     q14     q15
q12  {res}0.2477
{txt}q13  {res}0.0096  0.1255
{txt}q14  {res}0.0473  0.0116  0.1730
{txt}q15  {res}0.0543  0.0005  0.0208  0.2477

{txt}Pairwise number of observations

      q12   q13   q14   q15
q12  {res}1088
{txt}q13  {res}1086  1088
{txt}q14  {res}1087  1087  1089
{txt}q15  {res}1082  1082  1084  1085
{txt}
{com}. 
. /* Domain-Specific Knowldge of GW "causes" */
. cor q62 q63 q66 q67
{txt}(obs=1085)

             {c |}      q62      q63      q66      q67
{hline 13}{c +}{hline 36}
         q62 {c |}{res}   1.0000
         {txt}q63 {c |}{res}   0.1959   1.0000
         {txt}q66 {c |}{res}   0.1202   0.0779   1.0000
         {txt}q67 {c |}{res}   0.0701  -0.0175   0.0895   1.0000

{txt}
{com}. factor q62 q63 q66 q67  
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.33173      0.27333            2.4835       2.4835
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.05840      0.14591            0.4372       2.9207
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08752      0.08152           -0.6552       2.2655
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16904            .           -1.2655       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}   75.58{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0304}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8591}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3171}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1234}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8842}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2675}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0928}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9198}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1400}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9468}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q62 q63 q66 q67,detail gen (gw_know)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201437
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2859

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2407
{txt}q63  {res} 0.0436   0.2077
{txt}q66  {res} 0.0293   0.0172   0.2465
{txt}q67  {res} 0.0149  -0.0034   0.0193   0.1905

{txt}Pairwise number of observations

      q62   q63   q66   q67
q62  {res}1088
{txt}q63  {res}1088  1089
{txt}q66  {res}1085  1086  1086
{txt}q67  {res}1086  1087  1086  1087
{txt}
{com}. 
. /* Domain-specific by partisans and ideology */ 
. alpha q62 q63 q66 q67 if pid==0, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201991
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2817

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2334
{txt}q63  {res} 0.0440   0.2301
{txt}q66  {res} 0.0134   0.0399   0.2419
{txt}q67  {res} 0.0141  -0.0145   0.0242   0.1996

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}253
{txt}q63  {res}253  253
{txt}q66  {res}252  252  252
{txt}q67  {res}252  252  252  252
{txt}
{com}. alpha q62 q63 q66 q67 if pid==2, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0186036
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2793

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2434
{txt}q63  {res}0.0349  0.1789
{txt}q66  {res}0.0375  0.0057  0.2507
{txt}q67  {res}0.0143  0.0025  0.0167  0.1696

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}357
{txt}q63  {res}357  357
{txt}q66  {res}356  356  356
{txt}q67  {res}357  357  356  357
{txt}
{com}. 
. alpha q62 q63 q66 q67 if ideo==1, detail

{txt}Test scale = mean(unstandardized items)
Reversed item: {res: q63}

Average interitem covariance:{col 34}{res}  .015729
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2470

{txt}Interitem covariances (reverse applied) (obs=58 in all pairs)

         q62      q63      q66      q67
q62  {res} 0.2396
{txt}q63  {res}-0.0163   0.1770
{txt}q66  {res} 0.0333   0.0284   0.2468
{txt}q67  {res} 0.0079   0.0230   0.0181   0.1670
{txt}
{com}. alpha q62 q63 q66 q67 if ideo==7, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0343651
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.4037

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2241
{txt}q63  {res}0.0546  0.2495
{txt}q66  {res}0.0074  0.0527  0.2490
{txt}q67  {res}0.0309  0.0067  0.0536  0.2269

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}117
{txt}q63  {res}117  118
{txt}q66  {res}116  117  117
{txt}q67  {res}116  117  117  117
{txt}
{com}. 
. 
. ** Descriptive analysis  
. ** Correlation between pid ideo and gw_know education 
. spearman gw_know education

{txt} Number of obs = {res}   1077
{txt}Spearman's rho = {res}     -0.0036

{txt}Test of Ho: gw_know and education are independent
    Prob > |t| = {res}      0.9070
{txt}
{com}. spearman gw_know ideo

{txt} Number of obs = {res}   1044
{txt}Spearman's rho = {res}     -0.0695

{txt}Test of Ho: gw_know and ideo are independent
    Prob > |t| = {res}      0.0247
{txt}
{com}. spearman gw_know pid

{txt} Number of obs = {res}   1089
{txt}Spearman's rho = {res}      0.0650

{txt}Test of Ho: gw_know and pid are independent
    Prob > |t| = {res}      0.0320
{txt}
{com}. spearman ideo pid

{txt} Number of obs = {res}   1047
{txt}Spearman's rho = {res}     -0.5449

{txt}Test of Ho: ideo and pid are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman gw_know sci_obknowledge

{txt} Number of obs = {res}   1088
{txt}Spearman's rho = {res}      0.1123

{txt}Test of Ho: gw_know and sci_obknowledge are independent
    Prob > |t| = {res}      0.0002
{txt}
{com}. 
. *********************************************
. ///* 1 - |NEP - HEP| CORE VALUE CONFLICT *///
> *********************************************
. /*Recode HEP and NEP components to 0 to 1 scale with higher values indicating pro-enviornmental/human paradigm*/
. recode q20 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q20: 1063 changes made)

{com}. recode q21 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q21: 1057 changes made)

{com}. recode q22 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q22: 1064 changes made)

{com}. recode q23 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q23: 1026 changes made)

{com}. recode q24 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q24: 1079 changes made)

{com}. recode q25 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q25: 1069 changes made)

{com}. recode q26 4 = 0 3 = .33 2 = .67
{txt}(q26: 1020 changes made)

{com}. recode q27 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q27: 1040 changes made)

{com}. recode q28 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q28: 1044 changes made)

{com}. recode q29 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q29: 1064 changes made)

{com}. recode q30 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q30: 1053 changes made)

{com}. recode q31 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q31: 1073 changes made)

{com}. recode q32 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q32: 1046 changes made)

{com}. recode q33 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q33: 1040 changes made)

{com}. 
. /*NEP*/
. /* Range 1 (high environmental concern) to 0 (environmental concern) */
. alpha q20 q22 q24 q29 q31 q33, detail gen (nep)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0202498
{txt}Number of items in the scale:{col 34}{res}        6
{txt}Scale reliability coefficient:{col 34}{res}   0.7804

{txt}Interitem covariances (obs=pairwise, see below)

        q20     q22     q24     q29     q31     q33
q20  {res}0.0745
{txt}q22  {res}0.0187  0.0540
{txt}q24  {res}0.0218  0.0249  0.0527
{txt}q29  {res}0.0243  0.0120  0.0145  0.0474
{txt}q31  {res}0.0172  0.0206  0.0207  0.0159  0.0437
{txt}q33  {res}0.0241  0.0229  0.0285  0.0162  0.0216  0.0545

{txt}Pairwise number of observations

      q20   q22   q24   q29   q31   q33
q20  {res}1063
{txt}q22  {res}1042  1064
{txt}q24  {res}1051  1052  1079
{txt}q29  {res}1041  1041  1052  1064
{txt}q31  {res}1048  1051  1061  1050  1073
{txt}q33  {res}1019  1023  1029  1016  1028  1040
{txt}
{com}. 
. ********************************************************
. ///* 1 - |NEP - ECONOMIC PRIORITIES| VALUE CONFLICT *///
> ********************************************************
. /* Higher values indicate greater support for economy*/
. recode q7 4=1 3=.67 2=.33 1=0
{txt}(q7: 1051 changes made)

{com}. recode q9 4=0 3=.33 2=.67 
{txt}(q9: 948 changes made)

{com}. alpha q7 q9, detail gen(economy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0282268
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.5409

{txt}Interitem covariances (obs=pairwise, see below)

        q7      q9
q7  {res}0.0722
{txt}q9  {res}0.0282  0.0801

{txt}Pairwise number of observations

      q7    q9
q7  {res}1051
{txt}q9  {res}1021  1052
{txt}
{com}. gen nepvecon = 1 - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. 
. ** Pluralism and interactions
. ** create value pluralism 
. gen pluralism = ((nep + economy)/2) - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. ** create interaction of education and value pluralism
. gen educ_plural = education * pluralism 
{txt}(23 missing values generated)

{com}. 
. ** create interaction of global warming knowledge and value pluralism 
. gen  gwknow_plural = gw_know * pluralism 
{txt}(15 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_plural = pid * pluralism 
{txt}(12 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_gw_pluralism = pid * gwknow_plural
{txt}(15 missing values generated)

{com}. 
. 
. ***************************************************
. ///* CLIMATE CHANGE POLICY DEPENDENT VARIABLES *///
> ***************************************************
. /* In this area we create the dependent variables.  These are based upon policy preferences  */
. /* with regard to solving CC problems.  They are on a four-point scale */
. 
. /* First, combine q93_v1 and q93_v2, as they are conceptually the same, but differently worded.  This will */
. /* allow us to have a variable comparable in number of observations to the others */
. 
. recode q93_v1 .=0
{txt}(q93_v1: 595 changes made)

{com}. recode q93_v2 .=0
{txt}(q93_v2: 528 changes made)

{com}. gen q93 = q93_v1 + q93_v2
{txt}
{com}. /* Recode 0 in q93 to missing value so that it is not included */
. recode q93 0=.
{txt}(q93: 30 changes made)

{com}. 
. /* Second, rename the variables of policy preferences */
. 
. rename q89 emission
{txt}
{com}. rename q90 tax_industry
{txt}
{com}. rename q91 tax_individuals
{txt}
{com}. rename q92 educatepublic
{txt}
{com}. rename q93 setprice
{txt}
{com}. rename q94 kyoto
{txt}
{com}. rename q95 law
{txt}
{com}. rename q96 renewable
{txt}
{com}. rename q97 methane
{txt}
{com}. rename q98 seawalls
{txt}
{com}. rename q99 vehicle
{txt}
{com}. rename q100 gas
{txt}
{com}. 
. **********************
. ** FACTOR ANALYSIS 
. ***********************
. ** Outcome
. polychoric emission tax_industry tax_individuals educatepublic setprice kyoto law renewable methane seawalls vehicle gas
{res}
{txt}Polychoric correlation matrix

                        emission     tax_industry  tax_individuals    educatepublic         setprice
       emission  {res}              1
{txt}   tax_industry  {res}      .38294996                1
{txt}tax_individuals  {res}       .2817427        .65884566                1
{txt}  educatepublic  {res}      .42596698        .56156444        .45638559                1
{txt}       setprice  {res}      .38015571        .44741967        .39705273        .51586584                1
{txt}          kyoto  {res}      .28502789        .59065392        .56094995        .56813167        .55243934
{txt}            law  {res}      .24114177         .5510928         .5248511        .54684893        .44766774
{txt}      renewable  {res}      .31381857        .43031226        .29588311        .55078627        .44040616
{txt}        methane  {res}      .29683583        .51012867        .43641356        .50204919        .44330616
{txt}       seawalls  {res}      .19813302        .35309224        .32577293        .32791816        .24427951
{txt}        vehicle  {res}      .32556788        .53788956        .46732339        .58583267        .46884118
{txt}            gas  {res}      .19821008        .41686889        .53847394        .38795639        .34317912

                 {txt}          kyoto              law        renewable          methane         seawalls
          kyoto  {res}              1
{txt}            law  {res}      .66226403                1
{txt}      renewable  {res}      .47118613        .43726816                1
{txt}        methane  {res}      .59207183         .4820782        .43115455                1
{txt}       seawalls  {res}      .30652183        .30504036        .26048449        .44731962                1
{txt}        vehicle  {res}      .60690978         .6719865        .51398761        .50619442        .30972162
{txt}            gas  {res}      .47119116        .45347487        .25060855        .38205406        .21223494

                 {txt}        vehicle              gas
        vehicle  {res}              1
{txt}            gas  {res}      .39370877                1
{txt}
{com}. display r(sum_w)
{res}804
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=804)

Factor analysis/correlation{col 52}Number of obs    = {res}     804
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}      23

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      5.37100      4.94571            0.9655       0.9655
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.42529      0.17079            0.0765       1.0420
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.25450      0.07151            0.0458       1.0878
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.18299      0.10599            0.0329       1.1207
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.07701      0.08507            0.0138       1.1345
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.00807      0.03842           -0.0015       1.1330
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.04649      0.04775           -0.0084       1.1247
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.09424      0.01177           -0.0169       1.1077
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.10600      0.03677           -0.0191       1.0887
{txt}{col 5}{ralign 11:Factor10}  {c |}{res}     -0.14278      0.01656           -0.0257       1.0630
{txt}{col 5}{ralign 11:Factor11}  {c |}{res}     -0.15934      0.03189           -0.0286       1.0344
{txt}{col 5}{ralign 11:Factor12}  {c |}{res}     -0.19123            .           -0.0344       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}66{txt}) ={res} 4484.24{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:emission}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4491}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2025}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7573}}}{space 1}
{space 4}{space 0}{ralign 12:tax_industry}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7585}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1233}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4095}}}{space 1}
{space 4}{space 0}{ralign 12:tax_indivi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6943}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3522}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3939}}}{space 1}
{space 4}{space 0}{ralign 12:educatepub~c}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7497}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2001}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3980}}}{space 1}
{space 4}{space 0}{ralign 12:setprice}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6412}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1554}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5647}}}{space 1}
{space 4}{space 0}{ralign 12:kyoto}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8000}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0653}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3557}}}{space 1}
{space 4}{space 0}{ralign 12:law}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7549}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0911}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4218}}}{space 1}
{space 4}{space 0}{ralign 12:renewable}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6067}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2926}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5462}}}{space 1}
{space 4}{space 0}{ralign 12:methane}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6905}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0378}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5218}}}{space 1}
{space 4}{space 0}{ralign 12:seawalls}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4442}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0019}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8027}}}{space 1}
{space 4}{space 0}{ralign 12:vehicle}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7559}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0877}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4209}}}{space 1}
{space 4}{space 0}{ralign 12:gas}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5615}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2712}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6112}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** economy
. polychoric q7 q9
{res}
{txt}Variables :  {res}q7 q9
{txt}Type :       {res}polychoric
{txt}Rho        = {res}.43917163
{txt}S.e.       = {res}.03623147
{txt}Goodness of fit tests:
Pearson G2 = {res}86.275575{txt}, Prob( >chi2({res}8{txt})) = {res}2.645e-15
{txt}LR X2      = {res}129.48324{txt}, Prob( >chi2({res}8{txt})) = {res}3.620e-24
{txt}
{com}. display r(sum_w)
{res}1021
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1021)

Factor analysis/correlation{col 52}Number of obs    = {res}    1021
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       1

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.63204      0.87834            1.6385       1.6385
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.24630            .           -0.6385       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}1{txt})  ={res}  218.45{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5622}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6840}}}{space 1}
{space 4}{space 0}{ralign 12:q9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5622}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6840}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. 
. ** nep
. polychoric q20 q22 q24 q29 q31 q33
{res}
{txt}Polychoric correlation matrix

           q20        q22        q24        q29        q31        q33
q20  {res}        1
{txt}q22  {res}.35273246          1
{txt}q24  {res}.41100203  .56053546          1
{txt}q29  {res}.48337277  .30003974  .36322066          1
{txt}q31  {res}.36026443  .52479819  .51631262  .43078282          1
{txt}q33  {res}.43420349  .50565599  .63654066  .39682939  .53613197          1
{txt}
{com}. display r(sum_w)
{res}970
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=970)

Factor analysis/correlation{col 52}Number of obs    = {res}     970
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}      11

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.71438      2.50286            1.0953       1.0953
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.21153      0.22010            0.0854       1.1806
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.00858      0.06956           -0.0035       1.1772
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.07814      0.07335           -0.0315       1.1457
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.15149      0.05798           -0.0611       1.0845
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.20947            .           -0.0845       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res} 2032.59{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q20}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5829}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2543}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5955}}}{space 1}
{space 4}{space 0}{ralign 12:q22}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6674}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1744}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5242}}}{space 1}
{space 4}{space 0}{ralign 12:q24}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7495}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1494}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4159}}}{space 1}
{space 4}{space 0}{ralign 12:q29}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5646}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2943}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5947}}}{space 1}
{space 4}{space 0}{ralign 12:q31}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6962}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0413}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5136}}}{space 1}
{space 4}{space 0}{ralign 12:q33}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7509}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0762}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4303}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** domain specific knowledge
. polychoric q62 q63 q66 q67
{res}
{txt}Polychoric correlation matrix

           q62        q63        q66        q67
q62  {res}        1
{txt}q63  {res}.33263921          1
{txt}q66  {res}.18998335  .13025676          1
{txt}q67  {res}.11927385  -.0313108  .15133712          1
{txt}
{com}. display r(sum_w)
{res}1085
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.59639      0.46859            1.6376       1.6376
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.12781      0.24662            0.3509       1.9885
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.11882      0.12238           -0.3263       1.6623
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.24120            .           -0.6623       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  215.13{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5101}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0365}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7385}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4384}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1797}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7756}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3364}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1495}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8645}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1757}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2680}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8973}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** network interest
. polychoric q81 q82 q83 q85
{res}
{txt}Polychoric correlation matrix

           q81        q82        q83        q85
q81  {res}        1
{txt}q82  {res}.78255932          1
{txt}q83  {res}.49032185   .5575268          1
{txt}q85  {res}.42353143  .50747353  .74889158          1
{txt}
{com}. display r(sum_w)
{res}1080
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.37269      1.97898            0.9665       0.9665
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.39371      0.53966            0.1604       1.1268
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.14594      0.01947           -0.0594       1.0674
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16541            .           -0.0674       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 2351.46{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7562}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3395}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3129}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8143}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2731}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2624}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7770}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2954}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3090}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7308}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3415}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3493}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** risk 
. polychoric q101 q102 q103
{res}
{txt}Polychoric correlation matrix

           q101       q102       q103
q101  {res}        1
{txt}q102  {res} .6984237          1
{txt}q103  {res}.76951202  .69065427          1
{txt}
{com}. display r(sum_w)
{res}980
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.05171      2.14663            1.1219       1.1219
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.09492      0.03310           -0.0519       1.0700
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.12802            .           -0.0700       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1648.29{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8513}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2754}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7824}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3879}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8456}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2850}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. 
. /****************
>     GLLAMM 
> ****************/
. 
. ** rename response/outcome variables
. rename emission item_1
{txt}
{com}. rename tax_industry item_2
{txt}
{com}. rename tax_individuals item_3
{txt}
{com}. rename educatepublic item_4
{txt}
{com}. rename kyoto item_5
{txt}
{com}. rename law item_6 
{txt}
{com}. rename renewable item_7
{txt}
{com}. rename methane item_8
{txt}
{com}. rename seawalls item_9
{txt}
{com}. rename vehicle item_10
{txt}
{com}. rename gas item_11
{txt}
{com}. rename setprice item_12
{txt}
{com}. 
. ** change data into long form 
. reshape long item_, i(id) j(item)
{txt}(note: j = 1 2 3 4 5 6 7 8 9 10 11 12)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1093   {txt}->{res}   13116
{txt}Number of variables            {res}     264   {txt}->{res}     254
{txt}j variable (12 values)                    ->   {res}item
{txt}xij variables:
              {res}item_1 item_2 ... item_12   {txt}->   {res}item_
{txt}{hline 77}

{com}. rename item_ y
{txt}
{com}. qui tab item, gen(d)
{txt}
{com}. 
. 
. *******************
. **ESTIMATION 
. ********************
. ** model specification 
. eq load: d1-d12
{txt}
{com}. eq thr: d2-d12
{txt}
{com}. eq f1: race gender education ideo pid risk nep economy network  
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** estimate structural model (no het) 
. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-10332.914
{txt}Iteration 1:    log likelihood = {res}-10212.018
{txt}Iteration 2:    log likelihood = {res}-9817.1439
{txt}Iteration 3:    log likelihood = {res}-9736.3635
{txt}Iteration 4:    log likelihood = {res}-9674.6901
{txt}Iteration 5:    log likelihood = {res}-9634.0603
{txt}Iteration 6:    log likelihood = {res}-9626.4219
{txt}Iteration 7:    log likelihood = {res}-9588.1926
{txt}Iteration 8:    log likelihood = {res}-9568.9766
{txt}Iteration 9:    log likelihood = {res}-9560.5889
{txt}Iteration 10:    log likelihood = {res}-9555.1883
{txt}Iteration 11:    log likelihood = {res}-9555.1883


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9555.1883{txt}  
Iteration 1:{col 16}log likelihood = {res}-9555.1883{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-9551.7492{txt}  
Iteration 3:{col 16}log likelihood = {res}-9551.5706{txt}  
Iteration 4:{col 16}log likelihood = {res}-9551.5462{txt}  
Iteration 5:{col 16}log likelihood = {res} -9551.546{txt}  
{res} 
{txt}number of level 1 units = {res}11378
{txt}number of level 2 units = {res}984
 
{txt}Condition Number = {res}463.5543
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9551.546
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .7451339{col 26}{space 2}  .210392{col 37}{space 1}    3.54{col 46}{space 3}0.000{col 54}{space 4} .3327731{col 67}{space 3} 1.157495
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.011406{col 26}{space 2} .1732398{col 37}{space 1}    5.84{col 46}{space 3}0.000{col 54}{space 4} .6718628{col 67}{space 3}  1.35095
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0198751{col 26}{space 2} .2352716{col 37}{space 1}   -0.08{col 46}{space 3}0.933{col 54}{space 4}-.4809989{col 67}{space 3} .4412488
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .5377152{col 26}{space 2} .2776643{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4}-.0064968{col 67}{space 3} 1.081927
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0756605{col 26}{space 2} .2402828{col 37}{space 1}   -0.31{col 46}{space 3}0.753{col 54}{space 4}-.5466062{col 67}{space 3} .3952852
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.027434{col 26}{space 2} .3308157{col 37}{space 1}   -3.11{col 46}{space 3}0.002{col 54}{space 4}-1.675821{col 67}{space 3}-.3790467
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1801287{col 26}{space 2}  .198026{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4}-.2079951{col 67}{space 3} .5682525
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .1077478{col 26}{space 2} .1487399{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.1837771{col 67}{space 3} .3992727
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4063134{col 26}{space 2} .2601679{col 37}{space 1}   -1.56{col 46}{space 3}0.118{col 54}{space 4}-.9162332{col 67}{space 3} .1036063
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.139433{col 26}{space 2} .1377279{col 37}{space 1}    8.27{col 46}{space 3}0.000{col 54}{space 4} .8694914{col 67}{space 3} 1.409375
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .378386{col 26}{space 2} .1696045{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0459673{col 67}{space 3} .7108047
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.579936{col 26}{space 2} .1477533{col 37}{space 1}  -10.69{col 46}{space 3}0.000{col 54}{space 4}-1.869527{col 67}{space 3}-1.290345
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.153663{col 26}{space 2} .1892272{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .7827845{col 67}{space 3} 1.524542
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.834036{col 26}{space 2} .1578506{col 37}{space 1}   11.62{col 46}{space 3}0.000{col 54}{space 4} 1.524655{col 67}{space 3} 2.143418
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0418775{col 26}{space 2}  .194187{col 37}{space 1}   -0.22{col 46}{space 3}0.829{col 54}{space 4}-.4224771{col 67}{space 3} .3387221
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.039863{col 26}{space 2} .2572166{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .5357273{col 67}{space 3} 1.543998
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .6451003{col 26}{space 2} .1931074{col 37}{space 1}    3.34{col 46}{space 3}0.001{col 54}{space 4} .2666167{col 67}{space 3} 1.023584
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5914408{col 26}{space 2} .1337978{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-.8536797{col 67}{space 3}-.3292019
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .7934983{col 26}{space 2} .1567447{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .4862843{col 67}{space 3} 1.100712
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5434328{col 26}{space 2} .1022819{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .3429641{col 67}{space 3} .7439016
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2120283{col 26}{space 2} .1948313{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4} -.169834{col 67}{space 3} .5938905
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.620675{col 26}{space 2} .1165784{col 37}{space 1}   13.90{col 46}{space 3}0.000{col 54}{space 4} 1.392185{col 67}{space 3} 1.849164
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6987398{col 26}{space 2}  .134786{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4}  .434564{col 67}{space 3} .9629156
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6086687{col 26}{space 2} .1255164{col 37}{space 1}   -4.85{col 46}{space 3}0.000{col 54}{space 4}-.8546763{col 67}{space 3} -.362661
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.221473{col 26}{space 2} .2196103{col 37}{space 1}    5.56{col 46}{space 3}0.000{col 54}{space 4} .7910449{col 67}{space 3} 1.651901
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.516314{col 26}{space 2} .1928113{col 37}{space 1}    7.86{col 46}{space 3}0.000{col 54}{space 4} 1.138411{col 67}{space 3} 1.894217
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3385631{col 26}{space 2} .2109261{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0748444{col 67}{space 3} .7519706
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.938847{col 26}{space 2} .3101768{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} 1.330912{col 67}{space 3} 2.546782
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.148836{col 26}{space 2} .2264271{col 37}{space 1}    5.07{col 46}{space 3}0.000{col 54}{space 4} .7050473{col 67}{space 3} 1.592625
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4191419{col 26}{space 2} .1346708{col 37}{space 1}   -3.11{col 46}{space 3}0.002{col 54}{space 4}-.6830918{col 67}{space 3}-.1551921
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.134065{col 26}{space 2}  .195243{col 37}{space 1}    5.81{col 46}{space 3}0.000{col 54}{space 4} .7513954{col 67}{space 3} 1.516734
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5013284{col 26}{space 2} .1334434{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .2397842{col 67}{space 3} .7628727
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .5417001{col 26}{space 2} .2157673{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .1188039{col 67}{space 3} .9645962
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9077994{col 26}{space 2} .1464678{col 37}{space 1}    6.20{col 46}{space 3}0.000{col 54}{space 4} .6207278{col 67}{space 3} 1.194871
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4517502{col 26}{space 2} .1576649{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1427327{col 67}{space 3} .7607678
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.574634{col 26}{space 2} .1347389{col 37}{space 1}   11.69{col 46}{space 3}0.000{col 54}{space 4} 1.310551{col 67}{space 3} 1.838717
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.10726685 (.02159871)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.5294877 (.27775509)
{txt}    d3: {res}2.0881651 (.23402374)
{txt}    d4: {res}2.5531669 (.28603195)
{txt}    d5: {res}3.2558197 (.37592917)
{txt}    d6: {res}2.5791841 (.29247669)
{txt}    d7: {res}1.5861007 (.18979404)
{txt}    d8: {res}2.1206027 (.24383515)
{txt}    d9: {res}1.0523978 (.14105346)
{txt}    d10: {res}2.5796651 (.28910142)
{txt}    d11: {res}1.466825 (.1734521)
{txt}    d12: {res}1.863061 (.21069342)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01358015 (.03339732)
{txt}    gender: {res}-.02390257 (.02450273)
{txt}    education: {res}.02949765 (.01209383)
{txt}    ideo: {res}-.02609763 (.00897327)
{txt}    pid: {res}.0298576 (.01929161)
{txt}    risk: {res}.56196283 (.08574343)
{txt}    nep: {res}.7591968 (.11161284)
{txt}    economy: {res}-.42698738 (.07069701)
{txt}    network: {res}.09020468 (.04107433)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,57]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .74513392   1.0114065  -.01987508    .5377152   -.0756605  -1.0274336    .1801287   .10774779  -.40631341

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.1394331   .37838604  -1.5799358    1.153663   1.8340362  -.04187753   1.0398626   .64510027  -.59144082

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .79349828   .54343281   .21202826   1.6206749   .69873979  -.60866867   1.2214731   1.5163142   .33856306

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  1.9388471   1.1488364  -.41914195   1.1340647   .50132844   .54170006   .90779938   .45175024   1.5746339

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  2.5294877   2.0881651   2.5531669   3.2558197   2.5791841   1.5861007   2.1206027   1.0523978   2.5796651

{txt}        id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:         f1:         f1:         f1:
           d11         d12          d1        race      gender   education        ideo         pid        risk
y1 {res}   1.466825    1.863061   .32751618  -.01358015  -.02390257   .02949765  -.02609763    .0298576   .56196283

{txt}            f1:         f1:         f1:
           nep     economy     network
y1 {res}   .7591968  -.42698738   .09020468
{reset}
{com}. 
. ** MODEL 1 (Baseline het model)
. ** specification of het  
. eq het: education gw_know pluralism 
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(a) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9537.0535
{txt}Iteration 1:    log likelihood = {res} -9524.984
{txt}Iteration 2:    log likelihood = {res}  -9518.13
{txt}Iteration 3:    log likelihood = {res}-9517.4906
{txt}Iteration 4:    log likelihood = {res}-9517.4899


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9517.4899{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9514.8079{txt}  (not concave)
Iteration 2:{col 16}log likelihood = {res}-9512.2355{txt}  
Iteration 3:{col 16}log likelihood = {res}-9511.6212{txt}  (not concave)
Iteration 4:{col 16}log likelihood = {res}-9511.4556{txt}  
Iteration 5:{col 16}log likelihood = {res}-9511.3871{txt}  
Iteration 6:{col 16}log likelihood = {res}-9511.3861{txt}  
Iteration 7:{col 16}log likelihood = {res}-9511.3861{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}433.07246
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9511.3861
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .7623919{col 26}{space 2} .2114548{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .3479482{col 67}{space 3} 1.176836
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.009434{col 26}{space 2} .1765804{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .6633427{col 67}{space 3} 1.355525
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}  .035116{col 26}{space 2}  .235261{col 37}{space 1}    0.15{col 46}{space 3}0.881{col 54}{space 4}-.4259872{col 67}{space 3} .4962191
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .5610703{col 26}{space 2} .2741768{col 37}{space 1}    2.05{col 46}{space 3}0.041{col 54}{space 4} .0236937{col 67}{space 3} 1.098447
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0502174{col 26}{space 2} .2364879{col 37}{space 1}   -0.21{col 46}{space 3}0.832{col 54}{space 4}-.5137252{col 67}{space 3} .4132903
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.003171{col 26}{space 2} .3300912{col 37}{space 1}   -3.04{col 46}{space 3}0.002{col 54}{space 4}-1.650138{col 67}{space 3}-.3562043
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1856133{col 26}{space 2} .1962372{col 37}{space 1}    0.95{col 46}{space 3}0.344{col 54}{space 4}-.1990044{col 67}{space 3} .5702311
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0646167{col 26}{space 2} .1511299{col 37}{space 1}    0.43{col 46}{space 3}0.669{col 54}{space 4}-.2315924{col 67}{space 3} .3608258
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.3829764{col 26}{space 2} .2593421{col 37}{space 1}   -1.48{col 46}{space 3}0.140{col 54}{space 4}-.8912775{col 67}{space 3} .1253248
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.126915{col 26}{space 2}   .14243{col 37}{space 1}    7.91{col 46}{space 3}0.000{col 54}{space 4} .8477574{col 67}{space 3} 1.406073
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .3598599{col 26}{space 2} .1693849{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0278717{col 67}{space 3} .6918481
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.572821{col 26}{space 2} .1628244{col 37}{space 1}   -9.66{col 46}{space 3}0.000{col 54}{space 4}-1.891951{col 67}{space 3}-1.253691
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.151368{col 26}{space 2} .1934675{col 37}{space 1}    5.95{col 46}{space 3}0.000{col 54}{space 4} .7721788{col 67}{space 3} 1.530558
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.828595{col 26}{space 2} .1723945{col 37}{space 1}   10.61{col 46}{space 3}0.000{col 54}{space 4} 1.490708{col 67}{space 3} 2.166482
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0079762{col 26}{space 2} .1950743{col 37}{space 1}   -0.04{col 46}{space 3}0.967{col 54}{space 4}-.3903148{col 67}{space 3} .3743624
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.028007{col 26}{space 2} .2565494{col 37}{space 1}    4.01{col 46}{space 3}0.000{col 54}{space 4}   .52518{col 67}{space 3} 1.530835
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}  .623472{col 26}{space 2} .1921657{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .2468342{col 67}{space 3}  1.00011
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5782328{col 26}{space 2} .1342823{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.8414212{col 67}{space 3}-.3150444
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .7737587{col 26}{space 2} .1576844{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4}  .464703{col 67}{space 3} 1.082814
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5379113{col 26}{space 2} .1044426{col 37}{space 1}    5.15{col 46}{space 3}0.000{col 54}{space 4} .3332075{col 67}{space 3}  .742615
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2131111{col 26}{space 2} .1924918{col 37}{space 1}    1.11{col 46}{space 3}0.268{col 54}{space 4}-.1641659{col 67}{space 3}  .590388
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.595998{col 26}{space 2} .1297165{col 37}{space 1}   12.30{col 46}{space 3}0.000{col 54}{space 4} 1.341759{col 67}{space 3} 1.850238
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .684323{col 26}{space 2} .1354451{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} .4188554{col 67}{space 3} .9497906
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.594135{col 26}{space 2} .1318852{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4}-.8526254{col 67}{space 3}-.3356447
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.214238{col 26}{space 2} .2248071{col 37}{space 1}    5.40{col 46}{space 3}0.000{col 54}{space 4}  .773624{col 67}{space 3} 1.654851
{txt}{space 10}d3 {c |}{col 14}{res}{space 2}   1.5263{col 26}{space 2} .2023895{col 37}{space 1}    7.54{col 46}{space 3}0.000{col 54}{space 4} 1.129624{col 67}{space 3} 1.922976
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3381674{col 26}{space 2} .2138314{col 37}{space 1}    1.58{col 46}{space 3}0.114{col 54}{space 4}-.0809344{col 67}{space 3} .7572692
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.918718{col 26}{space 2} .3171002{col 37}{space 1}    6.05{col 46}{space 3}0.000{col 54}{space 4} 1.297213{col 67}{space 3} 2.540223
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.120832{col 26}{space 2} .2304482{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4}  .669162{col 67}{space 3} 1.572502
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4385431{col 26}{space 2} .1351077{col 37}{space 1}   -3.25{col 46}{space 3}0.001{col 54}{space 4}-.7033494{col 67}{space 3}-.1737369
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.114946{col 26}{space 2} .1998755{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4}  .723197{col 67}{space 3} 1.506694
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5203201{col 26}{space 2} .1374128{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4}  .250996{col 67}{space 3} .7896443
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .5043704{col 26}{space 2} .2151382{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .0827074{col 67}{space 3} .9260334
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .8911236{col 26}{space 2} .1499474{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .5972321{col 67}{space 3} 1.185015
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4205023{col 26}{space 2} .1571906{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} .1124144{col 67}{space 3} .7285902
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.611963{col 26}{space 2} .1552748{col 37}{space 1}   10.38{col 46}{space 3}0.000{col 54}{space 4}  1.30763{col 67}{space 3} 1.916296
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.00744777 (.00945922)
{txt}    gw_know: {res}.08562816 (.03783777)
{txt}    pluralism: {res}-.24218596 (.0382817)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.11930829 (.0252938)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4300278 (.26173584)
{txt}    d3: {res}2.0161812 (.22203287)
{txt}    d4: {res}2.4836551 (.27263413)
{txt}    d5: {res}3.0910184 (.35083386)
{txt}    d6: {res}2.4622934 (.27457672)
{txt}    d7: {res}1.5304861 (.17988942)
{txt}    d8: {res}2.0279418 (.22930179)
{txt}    d9: {res}1.0559948 (.13789705)
{txt}    d10: {res}2.4671975 (.27140325)
{txt}    d11: {res}1.3841455 (.16219382)
{txt}    d12: {res}1.7846256 (.19831601)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01253239 (.03520066)
{txt}    gender: {res}-.02447381 (.02590584)
{txt}    education: {res}.03242137 (.01292169)
{txt}    ideo: {res}-.02742904 (.00948997)
{txt}    pid: {res}.03048353 (.02034425)
{txt}    risk: {res}.59308958 (.09265435)
{txt}    nep: {res}.80449805 (.12109805)
{txt}    economy: {res}-.46701631 (.07705695)
{txt}    network: {res}.09726008 (.04357165)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix b = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,60]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .76239193   1.0094339   .03511595   .56107032  -.05021745  -1.0031712   .18561331   .06461672  -.38297637

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.1269151   .35985988   -1.572821   1.1513682   1.8285952  -.00797617   1.0280075   .62347204  -.57823279

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}   .7737587   .53791125   .21311109   1.5959985   .68432302  -.59413505   1.2142377   1.5262998   .33816742

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  1.9187182   1.1208323  -.43854313   1.1149457   .52032015   .50437041   .89112362   .42050232   1.6119627

{txt}          lns1:       lns1:       lns1:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
     education     gw_know   pluralism          d2          d3          d4          d5          d6          d7
y1 {res}  .00744777   .08562816  -.24218596   2.4300278   2.0161812   2.4836551   3.0910184   2.4622934   1.5304861

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:
            d8          d9         d10         d11         d12          d1        race      gender   education
y1 {res}  2.0279418   1.0559948   2.4671975   1.3841455   1.7846256   .34541032  -.01253239  -.02447381   .03242137

{txt}            f1:         f1:         f1:         f1:         f1:         f1:
          ideo         pid        risk         nep     economy     network
y1 {res} -.02742904   .03048353   .59308958   .80449805  -.46701631   .09726008
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** MODEL 2 (domain-specific * pluralism) 
. ** specification of het  
. eq het: education gw_know pluralism gwknow_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9511.3861
{txt}Iteration 1:    log likelihood = {res} -9511.092
{txt}Iteration 2:    log likelihood = {res} -9511.092


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -9511.092{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res} -9511.092{txt}  
Iteration 2:{col 16}log likelihood = {res}-9509.4938{txt}  
Iteration 3:{col 16}log likelihood = {res}-9509.4871{txt}  
Iteration 4:{col 16}log likelihood = {res}-9509.4871{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}440.49026
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9509.4871
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .7372273{col 26}{space 2} .2056849{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .3340923{col 67}{space 3} 1.140362
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} .9793274{col 26}{space 2}  .172384{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .6414609{col 67}{space 3} 1.317194
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0338904{col 26}{space 2} .2289273{col 37}{space 1}    0.15{col 46}{space 3}0.882{col 54}{space 4}-.4147988{col 67}{space 3} .4825797
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .5432388{col 26}{space 2}  .267639{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4}  .018676{col 67}{space 3} 1.067802
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0469112{col 26}{space 2}  .230151{col 37}{space 1}   -0.20{col 46}{space 3}0.838{col 54}{space 4}-.4979989{col 67}{space 3} .4041766
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.9787274{col 26}{space 2} .3220435{col 37}{space 1}   -3.04{col 46}{space 3}0.002{col 54}{space 4}-1.609921{col 67}{space 3}-.3475338
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1811156{col 26}{space 2} .1908133{col 37}{space 1}    0.95{col 46}{space 3}0.343{col 54}{space 4}-.1928716{col 67}{space 3} .5551028
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0583282{col 26}{space 2} .1470499{col 37}{space 1}    0.40{col 46}{space 3}0.692{col 54}{space 4}-.2298844{col 67}{space 3} .3465408
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.3733231{col 26}{space 2} .2521035{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-.8674369{col 67}{space 3} .1207906
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.090944{col 26}{space 2} .1395211{col 37}{space 1}    7.82{col 46}{space 3}0.000{col 54}{space 4} .8174873{col 67}{space 3}   1.3644
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .3501524{col 26}{space 2}  .164705{col 37}{space 1}    2.13{col 46}{space 3}0.034{col 54}{space 4} .0273366{col 67}{space 3} .6729683
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.525146{col 26}{space 2} .1596948{col 37}{space 1}   -9.55{col 46}{space 3}0.000{col 54}{space 4}-1.838142{col 67}{space 3} -1.21215
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.116285{col 26}{space 2} .1885385{col 37}{space 1}    5.92{col 46}{space 3}0.000{col 54}{space 4} .7467563{col 67}{space 3} 1.485814
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.772799{col 26}{space 2}  .170006{col 37}{space 1}   10.43{col 46}{space 3}0.000{col 54}{space 4} 1.439593{col 67}{space 3} 2.106004
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} -.007475{col 26}{space 2}  .189907{col 37}{space 1}   -0.04{col 46}{space 3}0.969{col 54}{space 4}-.3796859{col 67}{space 3} .3647359
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.000085{col 26}{space 2} .2507836{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .5085583{col 67}{space 3} 1.491612
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .6057152{col 26}{space 2}  .187452{col 37}{space 1}    3.23{col 46}{space 3}0.001{col 54}{space 4} .2383161{col 67}{space 3} .9731144
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5609167{col 26}{space 2} .1309244{col 37}{space 1}   -4.28{col 46}{space 3}0.000{col 54}{space 4}-.8175238{col 67}{space 3}-.3043096
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .7500972{col 26}{space 2} .1538998{col 37}{space 1}    4.87{col 46}{space 3}0.000{col 54}{space 4} .4484591{col 67}{space 3} 1.051735
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5213694{col 26}{space 2} .1017373{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4}  .321968{col 67}{space 3} .7207708
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2055179{col 26}{space 2} .1869048{col 37}{space 1}    1.10{col 46}{space 3}0.272{col 54}{space 4}-.1608089{col 67}{space 3} .5718446
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.545456{col 26}{space 2} .1284568{col 37}{space 1}   12.03{col 46}{space 3}0.000{col 54}{space 4} 1.293685{col 67}{space 3} 1.797227
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6633629{col 26}{space 2} .1320543{col 37}{space 1}    5.02{col 46}{space 3}0.000{col 54}{space 4} .4045412{col 67}{space 3} .9221847
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.5758404{col 26}{space 2} .1279641{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4}-.8266454{col 67}{space 3}-.3250353
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.178693{col 26}{space 2} .2188157{col 37}{space 1}    5.39{col 46}{space 3}0.000{col 54}{space 4} .7498218{col 67}{space 3} 1.607564
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.483523{col 26}{space 2} .1979929{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} 1.095464{col 67}{space 3} 1.871582
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3316851{col 26}{space 2} .2081049{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4} -.076193{col 67}{space 3} .7395631
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.871631{col 26}{space 2} .3102181{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} 1.263614{col 67}{space 3} 2.479647
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.093785{col 26}{space 2}  .224902{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4} .6529849{col 67}{space 3} 1.534584
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4221394{col 26}{space 2} .1315833{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-.6800379{col 67}{space 3}-.1642408
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.086705{col 26}{space 2} .1950146{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .7044837{col 67}{space 3} 1.468927
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5054918{col 26}{space 2}   .13354{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .2437581{col 67}{space 3} .7672254
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .4897156{col 26}{space 2} .2089374{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4} .0802059{col 67}{space 3} .8992253
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .8663668{col 26}{space 2} .1461059{col 37}{space 1}    5.93{col 46}{space 3}0.000{col 54}{space 4} .5800045{col 67}{space 3} 1.152729
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .409707{col 26}{space 2} .1527913{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} .1102416{col 67}{space 3} .7091723
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.561159{col 26}{space 2} .1526029{col 37}{space 1}   10.23{col 46}{space 3}0.000{col 54}{space 4} 1.262063{col 67}{space 3} 1.860255
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.00885879 (.00949911)
{txt}    gw_know: {res}.01065868 (.05392746)
{txt}    pluralism: {res}-.37300653 (.0774983)
{txt}    gwknow_plural: {res}.27836884 (.14287065)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.1113126 (.02403667)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4358007 (.26331803)
{txt}    d3: {res}2.0248289 (.22391933)
{txt}    d4: {res}2.4969681 (.27515576)
{txt}    d5: {res}3.1149498 (.35490743)
{txt}    d6: {res}2.4773597 (.277367)
{txt}    d7: {res}1.5384484 (.18140877)
{txt}    d8: {res}2.0380342 (.23137575)
{txt}    d9: {res}1.0623498 (.13911931)
{txt}    d10: {res}2.4745907 (.27320917)
{txt}    d11: {res}1.3862671 (.16311883)
{txt}    d12: {res}1.7916178 (.19984179)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01224348 (.03400671)
{txt}    gender: {res}-.02343093 (.02502755)
{txt}    education: {res}.03134285 (.01249689)
{txt}    ideo: {res}-.02645617 (.00918215)
{txt}    pid: {res}.02973518 (.01966044)
{txt}    risk: {res}.5714891 (.09023258)
{txt}    nep: {res}.7780292 (.11806637)
{txt}    economy: {res}-.45144444 (.07499254)
{txt}    network: {res}.09324605 (.04212845)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix c = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,61]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .73722726     .97932736     .03389043     .54323884    -.04691115    -.97872743     .18111563      .0583282

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.37332313     1.0909436     .35015244    -1.5251459      1.116285     1.7727988    -.00747502     1.0000852

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .60571524     -.5609167     .75009721     .52136936     .20551785     1.5454561     .66336294    -.57584035

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.1786928     1.4835228     .33168507     1.8716306     1.0937847    -.42213936     1.0867054     .50549177

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism  gwknow_plu~l
y1 {res}    .48971557     .86636684     .40970696     1.5611593     .00885879     .01065868    -.37300653     .27836884

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    2.4358007     2.0248289     2.4969681     3.1149498     2.4773597     1.5384484     2.0380342     1.0623498

{txt}          id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:           f1:
             d10           d11           d12            d1          race        gender     education          ideo
y1 {res}    2.4745907     1.3862671     1.7916178     .33363543    -.01224348    -.02343093     .03134285    -.02645617

{txt}              f1:           f1:           f1:           f1:           f1:
             pid          risk           nep       economy       network
y1 {res}    .02973518      .5714891      .7780292    -.45144444     .09324605
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. 
. ** MODEL 3 (education * pluralism) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9511.3861
{txt}Iteration 1:    log likelihood = {res}-9509.9791
{txt}Iteration 2:    log likelihood = {res}-9503.7298
{txt}Iteration 3:    log likelihood = {res}-9503.6021
{txt}Iteration 4:    log likelihood = {res}-9503.6021


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9503.6021{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9503.6021{txt}  
Iteration 2:{col 16}log likelihood = {res}-9502.6787{txt}  
Iteration 3:{col 16}log likelihood = {res}-9501.8029{txt}  
Iteration 4:{col 16}log likelihood = {res}-9501.7978{txt}  
Iteration 5:{col 16}log likelihood = {res}-9501.7978{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}434.26852
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9501.7978
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8981802{col 26}{space 2} .2465617{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4}  .414928{col 67}{space 3} 1.381432
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.184481{col 26}{space 2} .2088747{col 37}{space 1}    5.67{col 46}{space 3}0.000{col 54}{space 4}  .775094{col 67}{space 3} 1.593868
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0549344{col 26}{space 2}  .272446{col 37}{space 1}    0.20{col 46}{space 3}0.840{col 54}{space 4}-.4790499{col 67}{space 3} .5889187
{txt}{space 10}d5 {c |}{col 14}{res}{space 2}  .678239{col 26}{space 2} .3173657{col 37}{space 1}    2.14{col 46}{space 3}0.033{col 54}{space 4} .0562137{col 67}{space 3} 1.300264
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0286906{col 26}{space 2}   .27417{col 37}{space 1}   -0.10{col 46}{space 3}0.917{col 54}{space 4}-.5660539{col 67}{space 3} .5086726
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.143852{col 26}{space 2} .3842855{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-1.897038{col 67}{space 3}-.3906666
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .2373377{col 26}{space 2} .2277251{col 37}{space 1}    1.04{col 46}{space 3}0.297{col 54}{space 4}-.2089953{col 67}{space 3} .6836707
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0848383{col 26}{space 2} .1752112{col 37}{space 1}    0.48{col 46}{space 3}0.628{col 54}{space 4}-.2585693{col 67}{space 3} .4282458
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4145017{col 26}{space 2} .3008966{col 37}{space 1}   -1.38{col 46}{space 3}0.168{col 54}{space 4}-1.004248{col 67}{space 3} .1752447
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.321601{col 26}{space 2} .1719534{col 37}{space 1}    7.69{col 46}{space 3}0.000{col 54}{space 4} .9845786{col 67}{space 3} 1.658623
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4365657{col 26}{space 2} .1973272{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0498114{col 67}{space 3} .8233199
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.813359{col 26}{space 2} .1963365{col 37}{space 1}   -9.24{col 46}{space 3}0.000{col 54}{space 4}-2.198172{col 67}{space 3}-1.428547
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.339252{col 26}{space 2} .2278894{col 37}{space 1}    5.88{col 46}{space 3}0.000{col 54}{space 4} .8925972{col 67}{space 3} 1.785907
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.114409{col 26}{space 2} .2107025{col 37}{space 1}   10.04{col 46}{space 3}0.000{col 54}{space 4} 1.701439{col 67}{space 3} 2.527378
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0054957{col 26}{space 2} .2255983{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4}-.4366688{col 67}{space 3} .4476603
{txt}{space 10}d5 {c |}{col 14}{res}{space 2}  1.20837{col 26}{space 2} .2989787{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .6223828{col 67}{space 3} 1.794358
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7388677{col 26}{space 2} .2256435{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .2966145{col 67}{space 3} 1.181121
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6567252{col 26}{space 2} .1559685{col 37}{space 1}   -4.21{col 46}{space 3}0.000{col 54}{space 4}-.9624178{col 67}{space 3}-.3510326
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .9078723{col 26}{space 2} .1859183{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .5434792{col 67}{space 3} 1.272266
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6236826{col 26}{space 2} .1226982{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .3831986{col 67}{space 3} .8641666
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}  .269924{col 26}{space 2} .2241469{col 37}{space 1}    1.20{col 46}{space 3}0.229{col 54}{space 4} -.169396{col 67}{space 3} .7092439
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.846685{col 26}{space 2} .1618126{col 37}{space 1}   11.41{col 46}{space 3}0.000{col 54}{space 4} 1.529538{col 67}{space 3} 2.163831
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .8016253{col 26}{space 2} .1597525{col 37}{space 1}    5.02{col 46}{space 3}0.000{col 54}{space 4} .4885162{col 67}{space 3} 1.114734
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6792681{col 26}{space 2}  .152893{col 37}{space 1}   -4.44{col 46}{space 3}0.000{col 54}{space 4}-.9789329{col 67}{space 3}-.3796034
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.407997{col 26}{space 2} .2635617{col 37}{space 1}    5.34{col 46}{space 3}0.000{col 54}{space 4} .8914256{col 67}{space 3} 1.924568
{txt}{space 10}d3 {c |}{col 14}{res}{space 2}  1.77026{col 26}{space 2} .2412829{col 37}{space 1}    7.34{col 46}{space 3}0.000{col 54}{space 4} 1.297354{col 67}{space 3} 2.243166
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .4071091{col 26}{space 2} .2481593{col 37}{space 1}    1.64{col 46}{space 3}0.101{col 54}{space 4}-.0792743{col 67}{space 3} .8934924
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.235303{col 26}{space 2} .3740107{col 37}{space 1}    5.98{col 46}{space 3}0.000{col 54}{space 4} 1.502256{col 67}{space 3} 2.968351
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.325707{col 26}{space 2} .2731879{col 37}{space 1}    4.85{col 46}{space 3}0.000{col 54}{space 4} .7902682{col 67}{space 3} 1.861145
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4987876{col 26}{space 2} .1566653{col 37}{space 1}   -3.18{col 46}{space 3}0.001{col 54}{space 4}-.8058459{col 67}{space 3}-.1917293
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.309224{col 26}{space 2} .2368704{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .8449664{col 67}{space 3} 1.773481
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6082097{col 26}{space 2}  .160418{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .2937962{col 67}{space 3} .9226233
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .6100286{col 26}{space 2} .2515988{col 37}{space 1}    2.42{col 46}{space 3}0.015{col 54}{space 4}  .116904{col 67}{space 3} 1.103153
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.039745{col 26}{space 2} .1773006{col 37}{space 1}    5.86{col 46}{space 3}0.000{col 54}{space 4}  .692242{col 67}{space 3} 1.387248
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4973829{col 26}{space 2} .1832216{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1382752{col 67}{space 3} .8564905
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.856788{col 26}{space 2} .1877552{col 37}{space 1}    9.89{col 46}{space 3}0.000{col 54}{space 4} 1.488795{col 67}{space 3} 2.224782
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.04686356 (.01301377)
{txt}    gw_know: {res}.07234295 (.03795256)
{txt}    pluralism: {res}.26796181 (.1225137)
{txt}    educ_plural: {res}-.14741239 (.03377116)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.15643793 (.03459828)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4389837 (.26393906)
{txt}    d3: {res}2.0251894 (.22409584)
{txt}    d4: {res}2.4973289 (.27547764)
{txt}    d5: {res}3.101061 (.35341432)
{txt}    d6: {res}2.4909985 (.27896581)
{txt}    d7: {res}1.5301469 (.18080793)
{txt}    d8: {res}2.0433862 (.23201693)
{txt}    d9: {res}1.0601544 (.13896501)
{txt}    d10: {res}2.4916548 (.27520068)
{txt}    d11: {res}1.3920811 (.16370364)
{txt}    d12: {res}1.7974603 (.2005764)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01474748 (.04034557)
{txt}    gender: {res}-.02836325 (.02968461)
{txt}    education: {res}.03914456 (.0149527)
{txt}    ideo: {res}-.03138576 (.01091387)
{txt}    pid: {res}.03570183 (.02333885)
{txt}    risk: {res}.68282131 (.10871006)
{txt}    nep: {res}.92386071 (.14206127)
{txt}    economy: {res}-.53633054 (.09006765)
{txt}    network: {res}.10945714 (.04991894)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix d = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,61]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   .89818016    1.1844809    .05493441    .67823899   -.02869064   -1.1438523    .23733769    .08483828

{txt}         _cut11:      _cut11:      _cut11:      _cut11:      _cut12:      _cut12:      _cut12:      _cut12:
            d10          d11          d12        _cons           d2           d3           d4           d5
y1 {res}  -.41450171     1.321601    .43656566   -1.8133593    1.3392522    2.1144087    .00549573    1.2083703

{txt}         _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:
             d6           d7           d8           d9          d10          d11          d12        _cons
y1 {res}   .73886767   -.65672518    .90787233    .62368262    .26992396    1.8466846    .80162532   -.67926814

{txt}         _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}    1.407997    1.7702598    .40710906    2.2353035    1.3257065   -.49878759    1.3092238    .60820975

{txt}         _cut13:      _cut13:      _cut13:      _cut13:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .61002857    1.0397448    .49738287    1.8567881    .04686356    .07234295    .26796181   -.14741239

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   2.4389837    2.0251894    2.4973289     3.101061    2.4909985    1.5301469    2.0433862    1.0601544

{txt}         id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:          f1:          f1:
            d10          d11          d12           d1         race       gender    education         ideo
y1 {res}   2.4916548    1.3920811    1.7974603    .39552235   -.01474748   -.02836325    .03914456   -.03138576

{txt}             f1:          f1:          f1:          f1:          f1:
            pid         risk          nep      economy      network
y1 {res}   .03570183    .68282131    .92386071   -.53633054    .10945714
{reset}
{com}. 
. ** MODEL 4 (value trade-off) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural economy nep
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9501.7978
{txt}Iteration 1:    log likelihood = {res}-9495.2721
{txt}Iteration 2:    log likelihood = {res}-9488.9206
{txt}Iteration 3:    log likelihood = {res}-9485.3496
{txt}Iteration 4:    log likelihood = {res} -9485.257
{txt}Iteration 5:    log likelihood = {res} -9485.257


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -9485.257{txt}  
Iteration 1:{col 16}log likelihood = {res} -9485.257{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-9483.4547{txt}  
Iteration 3:{col 16}log likelihood = {res}-9482.5531{txt}  
Iteration 4:{col 16}log likelihood = {res}-9482.4554{txt}  
Iteration 5:{col 16}log likelihood = {res}-9482.4134{txt}  
Iteration 6:{col 16}log likelihood = {res}-9482.4127{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}539.89312
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9482.4127
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2}  1.27323{col 26}{space 2} .3446812{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4} .5976675{col 67}{space 3} 1.948793
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.651027{col 26}{space 2} .2993978{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} 1.064218{col 67}{space 3} 2.237836
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .1274793{col 26}{space 2} .3700351{col 37}{space 1}    0.34{col 46}{space 3}0.730{col 54}{space 4}-.5977761{col 67}{space 3} .8527347
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .9927125{col 26}{space 2} .4329249{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .1441954{col 67}{space 3}  1.84123
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .0431904{col 26}{space 2} .3731494{col 37}{space 1}    0.12{col 46}{space 3}0.908{col 54}{space 4} -.688169{col 67}{space 3} .7745497
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.525983{col 26}{space 2} .5441982{col 37}{space 1}   -2.80{col 46}{space 3}0.005{col 54}{space 4}-2.592592{col 67}{space 3} -.459374
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .3887879{col 26}{space 2} .3120806{col 37}{space 1}    1.25{col 46}{space 3}0.213{col 54}{space 4}-.2228788{col 67}{space 3} 1.000455
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .1318828{col 26}{space 2} .2423128{col 37}{space 1}    0.54{col 46}{space 3}0.586{col 54}{space 4}-.3430416{col 67}{space 3} .6068072
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.5265535{col 26}{space 2} .4176783{col 37}{space 1}   -1.26{col 46}{space 3}0.207{col 54}{space 4}-1.345188{col 67}{space 3} .2920809
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.797009{col 26}{space 2} .2546281{col 37}{space 1}    7.06{col 46}{space 3}0.000{col 54}{space 4} 1.297947{col 67}{space 3} 2.296071
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6473219{col 26}{space 2}  .273063{col 37}{space 1}    2.37{col 46}{space 3}0.018{col 54}{space 4} .1121283{col 67}{space 3} 1.182515
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.431202{col 26}{space 2} .2947914{col 37}{space 1}   -8.25{col 46}{space 3}0.000{col 54}{space 4}-3.008982{col 67}{space 3}-1.853421
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.847296{col 26}{space 2} .3265893{col 37}{space 1}    5.66{col 46}{space 3}0.000{col 54}{space 4} 1.207193{col 67}{space 3} 2.487399
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.846503{col 26}{space 2} .3210771{col 37}{space 1}    8.87{col 46}{space 3}0.000{col 54}{space 4} 2.217203{col 67}{space 3} 3.475802
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0505213{col 26}{space 2}  .303898{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4}-.5451078{col 67}{space 3} .6461503
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.684519{col 26}{space 2} .4135806{col 37}{space 1}    4.07{col 46}{space 3}0.000{col 54}{space 4} .8739163{col 67}{space 3} 2.495122
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.055031{col 26}{space 2} .3136118{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4}  .440363{col 67}{space 3} 1.669699
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.8509182{col 26}{space 2} .2168489{col 37}{space 1}   -3.92{col 46}{space 3}0.000{col 54}{space 4}-1.275934{col 67}{space 3}-.4259021
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.256206{col 26}{space 2} .2616878{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .7433078{col 67}{space 3} 1.769105
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .8344889{col 26}{space 2} .1747354{col 37}{space 1}    4.78{col 46}{space 3}0.000{col 54}{space 4} .4920138{col 67}{space 3} 1.176964
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .4345282{col 26}{space 2} .3096435{col 37}{space 1}    1.40{col 46}{space 3}0.161{col 54}{space 4}-.1723618{col 67}{space 3} 1.041418
{txt}{space 9}d11 {c |}{col 14}{res}{space 2}  2.45623{col 26}{space 2} .2512749{col 37}{space 1}    9.78{col 46}{space 3}0.000{col 54}{space 4}  1.96374{col 67}{space 3}  2.94872
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} 1.100302{col 26}{space 2} .2247259{col 37}{space 1}    4.90{col 46}{space 3}0.000{col 54}{space 4} .6598474{col 67}{space 3} 1.540757
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.8718343{col 26}{space 2} .2116078{col 37}{space 1}   -4.12{col 46}{space 3}0.000{col 54}{space 4}-1.286578{col 67}{space 3}-.4570905
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.938777{col 26}{space 2} .3769499{col 37}{space 1}    5.14{col 46}{space 3}0.000{col 54}{space 4} 1.199969{col 67}{space 3} 2.677585
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.385676{col 26}{space 2} .3521241{col 37}{space 1}    6.78{col 46}{space 3}0.000{col 54}{space 4} 1.695525{col 67}{space 3} 3.075826
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .5763233{col 26}{space 2} .3372042{col 37}{space 1}    1.71{col 46}{space 3}0.087{col 54}{space 4}-.0845847{col 67}{space 3} 1.237231
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 3.021492{col 26}{space 2} .5313208{col 37}{space 1}    5.69{col 46}{space 3}0.000{col 54}{space 4} 1.980122{col 67}{space 3} 4.062861
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.834208{col 26}{space 2} .3873554{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} 1.075005{col 67}{space 3}  2.59341
{txt}{space 10}d7 {c |}{col 14}{res}{space 2} -.635589{col 26}{space 2} .2186043{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4}-1.064046{col 67}{space 3}-.2071323
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.768954{col 26}{space 2} .3357998{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} 1.110798{col 67}{space 3} 2.427109
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .8097233{col 26}{space 2} .2253773{col 37}{space 1}    3.59{col 46}{space 3}0.000{col 54}{space 4} .3679919{col 67}{space 3} 1.251455
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .9065586{col 26}{space 2} .3520686{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .2165169{col 67}{space 3}   1.5966
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.371899{col 26}{space 2} .2516914{col 37}{space 1}    5.45{col 46}{space 3}0.000{col 54}{space 4} .8785933{col 67}{space 3} 1.865206
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6622431{col 26}{space 2} .2507658{col 37}{space 1}    2.64{col 46}{space 3}0.008{col 54}{space 4} .1707512{col 67}{space 3} 1.153735
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.535003{col 26}{space 2} .2901335{col 37}{space 1}    8.74{col 46}{space 3}0.000{col 54}{space 4} 1.966352{col 67}{space 3} 3.103654
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.05077521 (.01314451)
{txt}    gw_know: {res}.06376151 (.03838478)
{txt}    pluralism: {res}.03458178 (.13165795)
{txt}    educ_plural: {res}-.14038992 (.03439142)
{txt}    economy: {res}.36548548 (.06883774)
{txt}    nep: {res}.28837362 (.0670257)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.28882411 (.07080019)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4175415 (.26305684)
{txt}    d3: {res}1.9920933 (.22194172)
{txt}    d4: {res}2.467475 (.27323107)
{txt}    d5: {res}3.0491939 (.34926241)
{txt}    d6: {res}2.4758488 (.27802509)
{txt}    d7: {res}1.5377408 (.18185457)
{txt}    d8: {res}2.0154473 (.22992053)
{txt}    d9: {res}1.0432918 (.13832925)
{txt}    d10: {res}2.503008 (.27719666)
{txt}    d11: {res}1.3578545 (.16130372)
{txt}    d12: {res}1.768103 (.19828218)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01777864 (.0547527)
{txt}    gender: {res}-.03739283 (.0403694)
{txt}    education: {res}.05395316 (.02058615)
{txt}    ideo: {res}-.04273436 (.01501735)
{txt}    pid: {res}.04828261 (.03180641)
{txt}    risk: {res}.92770933 (.15570035)
{txt}    nep: {res}1.2934035 (.21029485)
{txt}    economy: {res}-.71779642 (.12733235)
{txt}    network: {res}.15141679 (.06845487)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix e = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,63]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.2732301     1.651027    .12747935    .99271253    .04319038   -1.5259829    .38878792    .13188283

{txt}         _cut11:      _cut11:      _cut11:      _cut11:      _cut12:      _cut12:      _cut12:      _cut12:
            d10          d11          d12        _cons           d2           d3           d4           d5
y1 {res}  -.52655351    1.7970091     .6473219   -2.4312016     1.847296    2.8465025    .05052126    1.6845193

{txt}         _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:
             d6           d7           d8           d9          d10          d11          d12        _cons
y1 {res}   1.0550308   -.85091815    1.2562065    .83448888     .4345282    2.4562301    1.1003021   -.87183426

{txt}         _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.9387767    2.3856757    .57632332    3.0214915    1.8342076   -.63558896    1.7689535    .80972331

{txt}         _cut13:      _cut13:      _cut13:      _cut13:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .90655858    1.3718995    .66224312    2.5350028    .05077521    .06376151    .03458178   -.14038992

{txt}           lns1:        lns1:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
        economy          nep           d2           d3           d4           d5           d6           d7
y1 {res}   .36548548    .28837362    2.4175415    1.9920933     2.467475    3.0491939    2.4758488    1.5377408

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:
             d8           d9          d10          d11          d12           d1         race       gender
y1 {res}   2.0154473    1.0432918     2.503008    1.3578545     1.768103    .53742358   -.01777864   -.03739283

{txt}             f1:          f1:          f1:          f1:          f1:          f1:          f1:
      education         ideo          pid         risk          nep      economy      network
y1 {res}   .05395316   -.04273436    .04828261    .92770933    1.2934035   -.71779642    .15141679
{reset}
{com}. 
. ** MODEL 5 (ideological strength) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural strength_ideo 
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(e) adapt 
{res}{err}initial vector: extra parameter lns1:economy found
specify skip option if necessary
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,63]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.2732301     1.651027    .12747935    .99271253    .04319038   -1.5259829    .38878792    .13188283

{txt}         _cut11:      _cut11:      _cut11:      _cut11:      _cut12:      _cut12:      _cut12:      _cut12:
            d10          d11          d12        _cons           d2           d3           d4           d5
y1 {res}  -.52655351    1.7970091     .6473219   -2.4312016     1.847296    2.8465025    .05052126    1.6845193

{txt}         _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:
             d6           d7           d8           d9          d10          d11          d12        _cons
y1 {res}   1.0550308   -.85091815    1.2562065    .83448888     .4345282    2.4562301    1.1003021   -.87183426

{txt}         _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.9387767    2.3856757    .57632332    3.0214915    1.8342076   -.63558896    1.7689535    .80972331

{txt}         _cut13:      _cut13:      _cut13:      _cut13:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .90655858    1.3718995    .66224312    2.5350028    .05077521    .06376151    .03458178   -.14038992

{txt}           lns1:        lns1:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
        economy          nep           d2           d3           d4           d5           d6           d7
y1 {res}   .36548548    .28837362    2.4175415    1.9920933     2.467475    3.0491939    2.4758488    1.5377408

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:
             d8           d9          d10          d11          d12           d1         race       gender
y1 {res}   2.0154473    1.0432918     2.503008    1.3578545     1.768103    .53742358   -.01777864   -.03739283

{txt}             f1:          f1:          f1:          f1:          f1:          f1:          f1:
      education         ideo          pid         risk          nep      economy      network
y1 {res}   .05395316   -.04273436    .04828261    .92770933    1.2934035   -.71779642    .15141679
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** MODEL 5 (ideological strength) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural strength_ideo 
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9501.7978
{txt}Iteration 1:    log likelihood = {res} -9501.208
{txt}Iteration 2:    log likelihood = {res}-9500.9027
{txt}Iteration 3:    log likelihood = {res} -9500.899


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -9500.899{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9500.8732{txt}  
Iteration 2:{col 16}log likelihood = {res}-9499.1995{txt}  
Iteration 3:{col 16}log likelihood = {res}-9499.1062{txt}  
Iteration 4:{col 16}log likelihood = {res}-9499.1056{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}439.51783
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9499.1056
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .9140637{col 26}{space 2} .2531135{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .4179702{col 67}{space 3} 1.410157
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.214837{col 26}{space 2} .2140392{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .7953281{col 67}{space 3} 1.634346
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}  .040019{col 26}{space 2} .2819037{col 37}{space 1}    0.14{col 46}{space 3}0.887{col 54}{space 4} -.512502{col 67}{space 3} .5925401
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .6900011{col 26}{space 2} .3265425{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0499896{col 67}{space 3} 1.330013
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0473378{col 26}{space 2} .2822431{col 37}{space 1}   -0.17{col 46}{space 3}0.867{col 54}{space 4}-.6005241{col 67}{space 3} .5058485
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.185751{col 26}{space 2} .3949984{col 37}{space 1}   -3.00{col 46}{space 3}0.003{col 54}{space 4}-1.959934{col 67}{space 3}-.4115688
{txt}{space 10}d8 {c |}{col 14}{res}{space 2}  .231558{col 26}{space 2} .2345224{col 37}{space 1}    0.99{col 46}{space 3}0.323{col 54}{space 4}-.2280975{col 67}{space 3} .6912134
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0810087{col 26}{space 2} .1809909{col 37}{space 1}    0.45{col 46}{space 3}0.654{col 54}{space 4}-.2737269{col 67}{space 3} .4357444
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4391956{col 26}{space 2} .3110863{col 37}{space 1}   -1.41{col 46}{space 3}0.158{col 54}{space 4}-1.048914{col 67}{space 3} .1705223
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.356579{col 26}{space 2} .1773279{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} 1.009022{col 67}{space 3} 1.704135
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4450697{col 26}{space 2} .2030925{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .0470157{col 67}{space 3} .8431237
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.863379{col 26}{space 2} .2031448{col 37}{space 1}   -9.17{col 46}{space 3}0.000{col 54}{space 4}-2.261535{col 67}{space 3}-1.465222
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.369296{col 26}{space 2} .2337082{col 37}{space 1}    5.86{col 46}{space 3}0.000{col 54}{space 4}  .911236{col 67}{space 3} 1.827356
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.156139{col 26}{space 2}  .215384{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} 1.733994{col 67}{space 3} 2.578284
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0029831{col 26}{space 2} .2322903{col 37}{space 1}    0.01{col 46}{space 3}0.990{col 54}{space 4}-.4522974{col 67}{space 3} .4582637
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.237648{col 26}{space 2} .3074709{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} .6350165{col 67}{space 3}  1.84028
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7521308{col 26}{space 2} .2307824{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .2998056{col 67}{space 3} 1.204456
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6772253{col 26}{space 2} .1619593{col 37}{space 1}   -4.18{col 46}{space 3}0.000{col 54}{space 4}-.9946596{col 67}{space 3} -.359791
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .9256182{col 26}{space 2} .1905892{col 37}{space 1}    4.86{col 46}{space 3}0.000{col 54}{space 4} .5520703{col 67}{space 3} 1.299166
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6390439{col 26}{space 2} .1263557{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .3913912{col 67}{space 3} .8866965
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2743649{col 26}{space 2} .2313205{col 37}{space 1}    1.19{col 46}{space 3}0.236{col 54}{space 4} -.179015{col 67}{space 3} .7277448
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.885604{col 26}{space 2}  .166299{col 37}{space 1}   11.34{col 46}{space 3}0.000{col 54}{space 4} 1.559664{col 67}{space 3} 2.211544
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .8197142{col 26}{space 2} .1641184{col 37}{space 1}    4.99{col 46}{space 3}0.000{col 54}{space 4}  .498048{col 67}{space 3}  1.14138
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6961025{col 26}{space 2} .1572395{col 37}{space 1}   -4.43{col 46}{space 3}0.000{col 54}{space 4}-1.004286{col 67}{space 3}-.3879187
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2}  1.43298{col 26}{space 2} .2699096{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .9039665{col 67}{space 3} 1.961993
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.799615{col 26}{space 2} .2462666{col 37}{space 1}    7.31{col 46}{space 3}0.000{col 54}{space 4} 1.316942{col 67}{space 3} 2.282289
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .4206381{col 26}{space 2} .2552644{col 37}{space 1}    1.65{col 46}{space 3}0.099{col 54}{space 4}-.0796708{col 67}{space 3}  .920947
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.294874{col 26}{space 2} .3854889{col 37}{space 1}    5.95{col 46}{space 3}0.000{col 54}{space 4} 1.539329{col 67}{space 3} 3.050418
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.348796{col 26}{space 2}  .279455{col 37}{space 1}    4.83{col 46}{space 3}0.000{col 54}{space 4}  .801074{col 67}{space 3} 1.896518
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4963888{col 26}{space 2} .1617917{col 37}{space 1}   -3.07{col 46}{space 3}0.002{col 54}{space 4}-.8134947{col 67}{space 3}-.1792829
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.333876{col 26}{space 2} .2428489{col 37}{space 1}    5.49{col 46}{space 3}0.000{col 54}{space 4} .8579011{col 67}{space 3} 1.809851
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6204835{col 26}{space 2} .1646872{col 37}{space 1}    3.77{col 46}{space 3}0.000{col 54}{space 4} .2977025{col 67}{space 3} .9432646
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .6324516{col 26}{space 2} .2595775{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4} .1236891{col 67}{space 3} 1.141214
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.054572{col 26}{space 2}  .181329{col 37}{space 1}    5.82{col 46}{space 3}0.000{col 54}{space 4} .6991742{col 67}{space 3} 1.409971
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .5087427{col 26}{space 2} .1880613{col 37}{space 1}    2.71{col 46}{space 3}0.007{col 54}{space 4} .1401493{col 67}{space 3}  .877336
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.900702{col 26}{space 2} .1935448{col 37}{space 1}    9.82{col 46}{space 3}0.000{col 54}{space 4} 1.521361{col 67}{space 3} 2.280043
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.04424174 (.0130594)
{txt}    gw_know: {res}.07556774 (.03800772)
{txt}    pluralism: {res}.24171786 (.12321005)
{txt}    educ_plural: {res}-.13967779 (.03398059)
{txt}    strength_ideo: {res}.02255586 (.00972813)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.16497759 (.03675546)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4318995 (.26404428)
{txt}    d3: {res}2.0093871 (.22324403)
{txt}    d4: {res}2.5005675 (.27662916)
{txt}    d5: {res}3.1014365 (.3547869)
{txt}    d6: {res}2.4805197 (.27879086)
{txt}    d7: {res}1.5451051 (.18285173)
{txt}    d8: {res}2.0370707 (.2322122)
{txt}    d9: {res}1.0587837 (.13932816)
{txt}    d10: {res}2.4999966 (.27693734)
{txt}    d11: {res}1.3825503 (.16345372)
{txt}    d12: {res}1.7941773 (.20101962)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01535144 (.04142829)
{txt}    gender: {res}-.02955319 (.03049163)
{txt}    education: {res}.04026975 (.01536747)
{txt}    ideo: {res}-.03255171 (.01125422)
{txt}    pid: {res}.03615359 (.02395335)
{txt}    risk: {res}.70070618 (.11201128)
{txt}    nep: {res}.94799817 (.14635986)
{txt}    economy: {res}-.55032925 (.09275035)
{txt}    network: {res}.11341466 (.05133252)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,62]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .91406368     1.2148372     .04001904     .69000112     -.0473378    -1.1857514     .23155798     .08100875

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.43919564     1.3565787     .44506969    -1.8633787     1.3692958     2.1561392     .00298315     1.2376485

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .75213078    -.67722532     .92561818     .63904385     .27436489      1.885604     .81971421     -.6961025

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.4329797     1.7996154      .4206381     2.2948738     1.3487958    -.49638881     1.3338762     .62048354

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism   educ_plural
y1 {res}    .63245155     1.0545724     .50874266     1.9007024     .04424174     .07556774     .24171786    -.13967779

{txt}            lns1:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
    strength_i~o            d2            d3            d4            d5            d6            d7            d8
y1 {res}    .02255586     2.4318995     2.0093871     2.5005675     3.1014365     2.4805197     1.5451051     2.0370707

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:
              d9           d10           d11           d12            d1          race        gender     education
y1 {res}    1.0587837     2.4999966     1.3825503     1.7941773     .40617433    -.01535144    -.02955319     .04026975

{txt}              f1:           f1:           f1:           f1:           f1:           f1:
            ideo           pid          risk           nep       economy       network
y1 {res}   -.03255171     .03615359     .70070618     .94799817    -.55032925     .11341466
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. **********************
. ** PARTISANSHIP MODELS
. **********************
. * MODEL 6 (partisan conditioning) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural pid pid_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9501.7978
{txt}Iteration 1:    log likelihood = {res}-9500.8939
{txt}Iteration 2:    log likelihood = {res}-9500.8358
{txt}Iteration 3:    log likelihood = {res}-9500.7987
{txt}Iteration 4:    log likelihood = {res}-9498.8696
{txt}Iteration 5:    log likelihood = {res}-9498.8696


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9498.8696{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9498.8696{txt}  
Iteration 2:{col 16}log likelihood = {res}-9498.4613{txt}  
Iteration 3:{col 16}log likelihood = {res}-9497.3319{txt}  
Iteration 4:{col 16}log likelihood = {res}-9496.8916{txt}  
Iteration 5:{col 16}log likelihood = {res}-9496.8401{txt}  
Iteration 6:{col 16}log likelihood = {res} -9496.836{txt}  
Iteration 7:{col 16}log likelihood = {res} -9496.836{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}433.95696
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9496.836
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8600089{col 26}{space 2} .2407149{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .3882164{col 67}{space 3} 1.331801
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.142107{col 26}{space 2} .2043213{col 37}{space 1}    5.59{col 46}{space 3}0.000{col 54}{space 4} .7416448{col 67}{space 3}  1.54257
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}  .049628{col 26}{space 2} .2665958{col 37}{space 1}    0.19{col 46}{space 3}0.852{col 54}{space 4}-.4728902{col 67}{space 3} .5721462
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .6264585{col 26}{space 2} .3109565{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4} .0169951{col 67}{space 3} 1.235922
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0554752{col 26}{space 2} .2689803{col 37}{space 1}   -0.21{col 46}{space 3}0.837{col 54}{space 4}-.5826668{col 67}{space 3} .4717164
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.121842{col 26}{space 2}  .375817{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4} -1.85843{col 67}{space 3}-.3852541
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .2048407{col 26}{space 2} .2236898{col 37}{space 1}    0.92{col 46}{space 3}0.360{col 54}{space 4}-.2335833{col 67}{space 3} .6432647
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0728864{col 26}{space 2} .1719024{col 37}{space 1}    0.42{col 46}{space 3}0.672{col 54}{space 4}-.2640361{col 67}{space 3}  .409809
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}  -.42495{col 26}{space 2} .2948975{col 37}{space 1}   -1.44{col 46}{space 3}0.150{col 54}{space 4}-1.002938{col 67}{space 3} .1530385
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.278508{col 26}{space 2} .1695621{col 37}{space 1}    7.54{col 46}{space 3}0.000{col 54}{space 4} .9461722{col 67}{space 3} 1.610843
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4125211{col 26}{space 2} .1935863{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4}  .033099{col 67}{space 3} .7919433
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.772657{col 26}{space 2}  .194667{col 37}{space 1}   -9.11{col 46}{space 3}0.000{col 54}{space 4}-2.154197{col 67}{space 3}-1.391117
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.291124{col 26}{space 2} .2225533{col 37}{space 1}    5.80{col 46}{space 3}0.000{col 54}{space 4} .8549274{col 67}{space 3}  1.72732
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.042382{col 26}{space 2} .2075164{col 37}{space 1}    9.84{col 46}{space 3}0.000{col 54}{space 4} 1.635658{col 67}{space 3} 2.449107
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} -.002814{col 26}{space 2}   .22027{col 37}{space 1}   -0.01{col 46}{space 3}0.990{col 54}{space 4}-.4345353{col 67}{space 3} .4289073
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.157572{col 26}{space 2} .2925254{col 37}{space 1}    3.96{col 46}{space 3}0.000{col 54}{space 4} .5842331{col 67}{space 3} 1.730912
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7059395{col 26}{space 2} .2204924{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .2737824{col 67}{space 3} 1.138097
{txt}{space 10}d7 {c |}{col 14}{res}{space 2} -.644316{col 26}{space 2} .1537122{col 37}{space 1}   -4.19{col 46}{space 3}0.000{col 54}{space 4}-.9455864{col 67}{space 3}-.3430456
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .8731269{col 26}{space 2} .1818562{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .5166953{col 67}{space 3} 1.229558
{txt}{space 10}d9 {c |}{col 14}{res}{space 2}  .598413{col 26}{space 2} .1202993{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .3626307{col 67}{space 3} .8341953
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2515036{col 26}{space 2} .2186851{col 37}{space 1}    1.15{col 46}{space 3}0.250{col 54}{space 4}-.1771112{col 67}{space 3} .6801185
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.781234{col 26}{space 2} .1606066{col 37}{space 1}   11.09{col 46}{space 3}0.000{col 54}{space 4} 1.466451{col 67}{space 3} 2.096018
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .7714923{col 26}{space 2} .1562048{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .4653365{col 67}{space 3} 1.077648
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6586125{col 26}{space 2} .1499023{col 37}{space 1}   -4.39{col 46}{space 3}0.000{col 54}{space 4}-.9524156{col 67}{space 3}-.3648094
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.349102{col 26}{space 2}  .257173{col 37}{space 1}    5.25{col 46}{space 3}0.000{col 54}{space 4}  .845052{col 67}{space 3} 1.853152
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.693349{col 26}{space 2}  .235999{col 37}{space 1}    7.18{col 46}{space 3}0.000{col 54}{space 4} 1.230799{col 67}{space 3} 2.155898
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3949359{col 26}{space 2} .2422161{col 37}{space 1}    1.63{col 46}{space 3}0.103{col 54}{space 4}-.0797989{col 67}{space 3} .8696707
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.163044{col 26}{space 2} .3673374{col 37}{space 1}    5.89{col 46}{space 3}0.000{col 54}{space 4} 1.443076{col 67}{space 3} 2.883012
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.284666{col 26}{space 2} .2673234{col 37}{space 1}    4.81{col 46}{space 3}0.000{col 54}{space 4} .7607214{col 67}{space 3}  1.80861
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4725722{col 26}{space 2} .1546848{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.7757489{col 67}{space 3}-.1693955
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.258819{col 26}{space 2} .2316574{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .8047786{col 67}{space 3} 1.712859
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5773759{col 26}{space 2}  .156627{col 37}{space 1}    3.69{col 46}{space 3}0.000{col 54}{space 4} .2703927{col 67}{space 3} .8843591
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .5939878{col 26}{space 2} .2454938{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .1128289{col 67}{space 3} 1.075147
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9834475{col 26}{space 2} .1732778{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .6438292{col 67}{space 3} 1.323066
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4769655{col 26}{space 2} .1786602{col 37}{space 1}    2.67{col 46}{space 3}0.008{col 54}{space 4}  .126798{col 67}{space 3}  .827133
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.811318{col 26}{space 2} .1870419{col 37}{space 1}    9.68{col 46}{space 3}0.000{col 54}{space 4} 1.444722{col 67}{space 3} 2.177913
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.05122242 (.01312734)
{txt}    gw_know: {res}.07833715 (.03811368)
{txt}    pluralism: {res}.31249132 (.13621484)
{txt}    educ_plural: {res}-.15865548 (.03400033)
{txt}    pid: {res}-.03976127 (.01908874)
{txt}    pid_plural: {res}-.01451444 (.05145915)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.15009887 (.03365227)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4195688 (.26158237)
{txt}    d3: {res}2.0049277 (.2216467)
{txt}    d4: {res}2.4914971 (.27443509)
{txt}    d5: {res}3.0904376 (.35183315)
{txt}    d6: {res}2.4831105 (.27766025)
{txt}    d7: {res}1.5400512 (.18163903)
{txt}    d8: {res}2.0333222 (.23060494)
{txt}    d9: {res}1.0514754 (.1379099)
{txt}    d10: {res}2.4837698 (.27397678)
{txt}    d11: {res}1.3779499 (.16211368)
{txt}    d12: {res}1.7895719 (.1996656)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01465093 (.03943651)
{txt}    gender: {res}-.02806784 (.0290792)
{txt}    education: {res}.0383502 (.01466531)
{txt}    ideo: {res}-.03070224 (.01069934)
{txt}    pid: {res}.03213966 (.02287867)
{txt}    risk: {res}.66833508 (.10717308)
{txt}    nep: {res}.9028874 (.14005536)
{txt}    economy: {res}-.52704248 (.08892169)
{txt}    network: {res}.10758988 (.04893724)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,63]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   .86000888    1.1421072    .04962797    .62645855    -.0554752   -1.1218418    .20484072    .07288645

{txt}         _cut11:      _cut11:      _cut11:      _cut11:      _cut12:      _cut12:      _cut12:      _cut12:
            d10          d11          d12        _cons           d2           d3           d4           d5
y1 {res}     -.42495    1.2785077    .41252114    -1.772657    1.2911238    2.0423821   -.00281399    1.1575724

{txt}         _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:
             d6           d7           d8           d9          d10          d11          d12        _cons
y1 {res}   .70593951     -.644316    .87312691      .598413    .25150364    1.7812343    .77149227   -.65861252

{txt}         _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.3491018    1.6933488    .39493591    2.1630442    1.2846657   -.47257218    1.2588187    .57737588

{txt}         _cut13:      _cut13:      _cut13:      _cut13:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}    .5939878    .98344745    .47696548    1.8113176    .05122242    .07833715    .31249132   -.15865548

{txt}           lns1:        lns1:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
            pid   pid_plural           d2           d3           d4           d5           d6           d7
y1 {res}  -.03976127   -.01451444    2.4195688    2.0049277    2.4914971    3.0904376    2.4831105    1.5400512

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:
             d8           d9          d10          d11          d12           d1         race       gender
y1 {res}   2.0333222    1.0514754    2.4837698    1.3779499    1.7895719    .38742595   -.01465093   -.02806784

{txt}             f1:          f1:          f1:          f1:          f1:          f1:          f1:
      education         ideo          pid         risk          nep      economy      network
y1 {res}    .0383502   -.03070224    .03213966    .66833508     .9028874   -.52704248    .10758988
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. tab pid 

{txt}suppose you were {c |}
   in the voting {c |}
   booth and you {c |}
  came across an {c |}
office for which {c |}
             two {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
      republican {c |}{res}      3,072       23.42       23.42
{txt}independent/else {c |}{res}      5,760       43.92       67.34
{txt}        democrat {c |}{res}      4,284       32.66      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}     13,116      100.00
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. gen democrat = 0 
{txt}
{com}. recode democrat 0=1 if pid==2
{txt}(democrat: 4284 changes made)

{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. gen republican = 0 
{txt}
{com}. recode republican 0=1 if pid==0
{txt}(republican: 3072 changes made)

{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. tab pid democrat

{txt}suppose you were {c |}
   in the voting {c |}
   booth and you {c |}
  came across an {c |}
office for which {c |}       democrat
             two {c |}         0          1 {c |}     Total
{hline 17}{c +}{hline 22}{c +}{hline 10}
      republican {c |}{res}     3,072          0 {txt}{c |}{res}     3,072 
{txt}independent/else {c |}{res}     5,760          0 {txt}{c |}{res}     5,760 
{txt}        democrat {c |}{res}         0      4,284 {txt}{c |}{res}     4,284 
{txt}{hline 17}{c +}{hline 22}{c +}{hline 10}
           Total {c |}{res}     8,832      4,284 {txt}{c |}{res}    13,116 

{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. tab pid republican

{txt}suppose you were {c |}
   in the voting {c |}
   booth and you {c |}
  came across an {c |}
office for which {c |}      republican
             two {c |}         0          1 {c |}     Total
{hline 17}{c +}{hline 22}{c +}{hline 10}
      republican {c |}{res}         0      3,072 {txt}{c |}{res}     3,072 
{txt}independent/else {c |}{res}     5,760          0 {txt}{c |}{res}     5,760 
{txt}        democrat {c |}{res}     4,284          0 {txt}{c |}{res}     4,284 
{txt}{hline 17}{c +}{hline 22}{c +}{hline 10}
           Total {c |}{res}    10,044      3,072 {txt}{c |}{res}    13,116 

{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. 
. ** generate interactions 
. gen dem_educ_plural = educ_plural * democrat
{txt}(276 missing values generated)

{com}. gen rep_educ_plural = educ_plural * republican
{txt}(276 missing values generated)

{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** MODEL 7 (democrat partisan conditioning) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural democrat republican dem_educ_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9501.7978
{txt}Iteration 1:    log likelihood = {res}-9497.1571
{txt}Iteration 2:    log likelihood = {res}-9495.7756
{txt}Iteration 3:    log likelihood = {res} -9495.613
{txt}Iteration 4:    log likelihood = {res}-9495.5578
{txt}Iteration 5:    log likelihood = {res}-9495.5578


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9495.5578{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9495.5578{txt}  
Iteration 2:{col 16}log likelihood = {res}-9493.7489{txt}  
Iteration 3:{col 16}log likelihood = {res}-9492.2484{txt}  
Iteration 4:{col 16}log likelihood = {res}-9492.1595{txt}  
Iteration 5:{col 16}log likelihood = {res} -9492.159{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}428.41787
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9492.159
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8666803{col 26}{space 2} .2425826{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .3912272{col 67}{space 3} 1.342133
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.142619{col 26}{space 2} .2060552{col 37}{space 1}    5.55{col 46}{space 3}0.000{col 54}{space 4} .7387583{col 67}{space 3}  1.54648
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0401949{col 26}{space 2} .2689696{col 37}{space 1}    0.15{col 46}{space 3}0.881{col 54}{space 4}-.4869759{col 67}{space 3} .5673657
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .6271966{col 26}{space 2} .3157393{col 37}{space 1}    1.99{col 46}{space 3}0.047{col 54}{space 4}  .008359{col 67}{space 3} 1.246034
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0679778{col 26}{space 2} .2720142{col 37}{space 1}   -0.25{col 46}{space 3}0.803{col 54}{space 4}-.6011158{col 67}{space 3} .4651601
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.138486{col 26}{space 2}  .379891{col 37}{space 1}   -3.00{col 46}{space 3}0.003{col 54}{space 4}-1.883059{col 67}{space 3}-.3939138
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1995688{col 26}{space 2}  .225616{col 37}{space 1}    0.88{col 46}{space 3}0.376{col 54}{space 4}-.2426304{col 67}{space 3} .6417679
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0655222{col 26}{space 2} .1738587{col 37}{space 1}    0.38{col 46}{space 3}0.706{col 54}{space 4}-.2752346{col 67}{space 3}  .406279
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4309633{col 26}{space 2} .2980594{col 37}{space 1}   -1.45{col 46}{space 3}0.148{col 54}{space 4}-1.015149{col 67}{space 3} .1532224
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.290795{col 26}{space 2} .1704323{col 37}{space 1}    7.57{col 46}{space 3}0.000{col 54}{space 4} .9567536{col 67}{space 3} 1.624836
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4127033{col 26}{space 2} .1954635{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0296018{col 67}{space 3} .7958047
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -1.79144{col 26}{space 2} .1963789{col 37}{space 1}   -9.12{col 46}{space 3}0.000{col 54}{space 4}-2.176336{col 67}{space 3}-1.406545
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.302274{col 26}{space 2} .2234161{col 37}{space 1}    5.83{col 46}{space 3}0.000{col 54}{space 4} .8643865{col 67}{space 3} 1.740162
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.064064{col 26}{space 2} .2072274{col 37}{space 1}    9.96{col 46}{space 3}0.000{col 54}{space 4} 1.657905{col 67}{space 3} 2.470222
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0123657{col 26}{space 2} .2216128{col 37}{space 1}   -0.06{col 46}{space 3}0.956{col 54}{space 4}-.4467189{col 67}{space 3} .4219875
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.172703{col 26}{space 2} .2965724{col 37}{space 1}    3.95{col 46}{space 3}0.000{col 54}{space 4} .5914319{col 67}{space 3} 1.753974
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7070926{col 26}{space 2} .2222428{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} .2715047{col 67}{space 3} 1.142681
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6559778{col 26}{space 2} .1546032{col 37}{space 1}   -4.24{col 46}{space 3}0.000{col 54}{space 4}-.9589944{col 67}{space 3}-.3529611
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .8766373{col 26}{space 2} .1825319{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .5188814{col 67}{space 3} 1.234393
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5959117{col 26}{space 2} .1212395{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4} .3582866{col 67}{space 3} .8335368
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2514869{col 26}{space 2} .2212016{col 37}{space 1}    1.14{col 46}{space 3}0.256{col 54}{space 4}-.1820603{col 67}{space 3} .6850341
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.800356{col 26}{space 2} .1596691{col 37}{space 1}   11.28{col 46}{space 3}0.000{col 54}{space 4} 1.487411{col 67}{space 3} 2.113302
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .7784641{col 26}{space 2} .1572107{col 37}{space 1}    4.95{col 46}{space 3}0.000{col 54}{space 4} .4703368{col 67}{space 3} 1.086592
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6630473{col 26}{space 2} .1519471{col 37}{space 1}   -4.36{col 46}{space 3}0.000{col 54}{space 4}-.9608582{col 67}{space 3}-.3652365
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.358479{col 26}{space 2} .2585565{col 37}{space 1}    5.25{col 46}{space 3}0.000{col 54}{space 4} .8517173{col 67}{space 3}  1.86524
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.709164{col 26}{space 2} .2367621{col 37}{space 1}    7.22{col 46}{space 3}0.000{col 54}{space 4} 1.245119{col 67}{space 3} 2.173209
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3879821{col 26}{space 2} .2439773{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.0902046{col 67}{space 3} .8661688
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.198576{col 26}{space 2} .3717872{col 37}{space 1}    5.91{col 46}{space 3}0.000{col 54}{space 4} 1.469886{col 67}{space 3} 2.927265
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.294153{col 26}{space 2} .2697412{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .7654699{col 67}{space 3} 1.822836
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4881133{col 26}{space 2} .1552045{col 37}{space 1}   -3.14{col 46}{space 3}0.002{col 54}{space 4}-.7923086{col 67}{space 3} -.183918
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.260508{col 26}{space 2}  .232751{col 37}{space 1}    5.42{col 46}{space 3}0.000{col 54}{space 4}  .804324{col 67}{space 3} 1.716691
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5712554{col 26}{space 2} .1581702{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} .2612475{col 67}{space 3} .8812634
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .5995292{col 26}{space 2} .2486611{col 37}{space 1}    2.41{col 46}{space 3}0.016{col 54}{space 4} .1121624{col 67}{space 3} 1.086896
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9946756{col 26}{space 2} .1744011{col 37}{space 1}    5.70{col 46}{space 3}0.000{col 54}{space 4} .6528557{col 67}{space 3} 1.336496
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4754861{col 26}{space 2} .1804624{col 37}{space 1}    2.63{col 46}{space 3}0.008{col 54}{space 4} .1217862{col 67}{space 3} .8291859
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.840604{col 26}{space 2} .1879304{col 37}{space 1}    9.79{col 46}{space 3}0.000{col 54}{space 4} 1.472267{col 67}{space 3}  2.20894
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.05072184 (.01316081)
{txt}    gw_know: {res}.08410287 (.03828397)
{txt}    pluralism: {res}.2897581 (.12242271)
{txt}    educ_plural: {res}-.14920189 (.03518151)
{txt}    democrat: {res}-.08516394 (.02793325)
{txt}    republican: {res}-.01862914 (.02508479)
{txt}    dem_educ_plural: {res}-.02163853 (.02152949)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.15540495 (.0344115)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4049473 (.25906071)
{txt}    d3: {res}1.9972653 (.22015139)
{txt}    d4: {res}2.4700957 (.27126666)
{txt}    d5: {res}3.0911343 (.35097038)
{txt}    d6: {res}2.4699032 (.27530316)
{txt}    d7: {res}1.5240055 (.17931342)
{txt}    d8: {res}2.0186523 (.22824525)
{txt}    d9: {res}1.0412904 (.13653719)
{txt}    d10: {res}2.4746696 (.27206198)
{txt}    d11: {res}1.3727967 (.16098284)
{txt}    d12: {res}1.7793744 (.19776287)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01499405 (.0401309)
{txt}    gender: {res}-.02853089 (.02959377)
{txt}    education: {res}.03892167 (.01489978)
{txt}    ideo: {res}-.03120552 (.01087594)
{txt}    pid: {res}.03230264 (.02318804)
{txt}    risk: {res}.68130051 (.108428)
{txt}    nep: {res}.91941305 (.14161604)
{txt}    economy: {res}-.53754483 (.09009046)
{txt}    network: {res}.10965915 (.04975578)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,64]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .86668027     1.1426192     .04019486     .62719663    -.06797782    -1.1384865     .19956878      .0655222

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.43096333     1.2907947     .41270326    -1.7914403      1.302274     2.0640635    -.01236574     1.1727032

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .70709264    -.65597776      .8766373     .59591171     .25148691     1.8003562     .77846414    -.66304735

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.3584787     1.7091637     .38798211     2.1985759      1.294153    -.48811329     1.2605075     .57125544

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism   educ_plural
y1 {res}    .59952921     .99467559     .47548605     1.8406036     .05072184     .08410287      .2897581    -.14920189

{txt}            lns1:         lns1:         lns1:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
        democrat    republican  dem_educ_p~l            d2            d3            d4            d5            d6
y1 {res}   -.08516394    -.01862914    -.02163853     2.4049473     1.9972653     2.4700957     3.0911343     2.4699032

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:
              d7            d8            d9           d10           d11           d12            d1          race
y1 {res}    1.5240055     2.0186523     1.0412904     2.4746696     1.3727967     1.7793744     .39421434    -.01499405

{txt}              f1:           f1:           f1:           f1:           f1:           f1:           f1:           f1:
          gender     education          ideo           pid          risk           nep       economy       network
y1 {res}   -.02853089     .03892167    -.03120552     .03230264     .68130051     .91941305    -.53754483     .10965915
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** MODEL 8 (repubican partisan conditioning) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural democrat republican rep_educ_plural
{txt}
{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(f) adapt 
{res}{err}initial vector: extra parameter lns1:dem_educ_plural found
specify skip option if necessary
{txt}
{com}. matrix g = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,64]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .86668027     1.1426192     .04019486     .62719663    -.06797782    -1.1384865     .19956878      .0655222

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.43096333     1.2907947     .41270326    -1.7914403      1.302274     2.0640635    -.01236574     1.1727032

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .70709264    -.65597776      .8766373     .59591171     .25148691     1.8003562     .77846414    -.66304735

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.3584787     1.7091637     .38798211     2.1985759      1.294153    -.48811329     1.2605075     .57125544

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism   educ_plural
y1 {res}    .59952921     .99467559     .47548605     1.8406036     .05072184     .08410287      .2897581    -.14920189

{txt}            lns1:         lns1:         lns1:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
        democrat    republican  dem_educ_p~l            d2            d3            d4            d5            d6
y1 {res}   -.08516394    -.01862914    -.02163853     2.4049473     1.9972653     2.4700957     3.0911343     2.4699032

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:
              d7            d8            d9           d10           d11           d12            d1          race
y1 {res}    1.5240055     2.0186523     1.0412904     2.4746696     1.3727967     1.7793744     .39421434    -.01499405

{txt}              f1:           f1:           f1:           f1:           f1:           f1:           f1:           f1:
          gender     education          ideo           pid          risk           nep       economy       network
y1 {res}   -.02853089     .03892167    -.03120552     .03230264     .68130051     .91941305    -.53754483     .10965915
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD05950.000000"
{txt}
{com}. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9501.7978
{txt}Iteration 1:    log likelihood = {res}-9496.6545
{txt}Iteration 2:    log likelihood = {res}-9494.8608
{txt}Iteration 3:    log likelihood = {res}-9494.4876
{txt}Iteration 4:    log likelihood = {res}-9494.4732
{txt}Iteration 5:    log likelihood = {res}-9494.4732


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9494.4732{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9494.4732{txt}  
Iteration 2:{col 16}log likelihood = {res}-9492.2936{txt}  
Iteration 3:{col 16}log likelihood = {res}-9491.6249{txt}  
Iteration 4:{col 16}log likelihood = {res}-9491.6073{txt}  
Iteration 5:{col 16}log likelihood = {res}-9491.6073{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}433.85341
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9491.6073
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8716308{col 26}{space 2} .2457803{col 37}{space 1}    3.55{col 46}{space 3}0.000{col 54}{space 4} .3899103{col 67}{space 3} 1.353351
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.158047{col 26}{space 2} .2092541{col 37}{space 1}    5.53{col 46}{space 3}0.000{col 54}{space 4} .7479167{col 67}{space 3} 1.568178
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0401725{col 26}{space 2} .2725487{col 37}{space 1}    0.15{col 46}{space 3}0.883{col 54}{space 4}-.4940131{col 67}{space 3}  .574358
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .6323628{col 26}{space 2} .3205097{col 37}{space 1}    1.97{col 46}{space 3}0.048{col 54}{space 4} .0041754{col 67}{space 3}  1.26055
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0700792{col 26}{space 2} .2764569{col 37}{space 1}   -0.25{col 46}{space 3}0.800{col 54}{space 4}-.6119248{col 67}{space 3} .4717665
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.155213{col 26}{space 2} .3875748{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-1.914846{col 67}{space 3}-.3955803
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1903073{col 26}{space 2} .2292466{col 37}{space 1}    0.83{col 46}{space 3}0.406{col 54}{space 4}-.2590078{col 67}{space 3} .6396224
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0643391{col 26}{space 2} .1764296{col 37}{space 1}    0.36{col 46}{space 3}0.715{col 54}{space 4}-.2814566{col 67}{space 3} .4101349
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4340004{col 26}{space 2} .3028567{col 37}{space 1}   -1.43{col 46}{space 3}0.152{col 54}{space 4}-1.027589{col 67}{space 3} .1595878
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.310586{col 26}{space 2} .1734724{col 37}{space 1}    7.56{col 46}{space 3}0.000{col 54}{space 4} .9705866{col 67}{space 3} 1.650586
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .412205{col 26}{space 2} .1987917{col 37}{space 1}    2.07{col 46}{space 3}0.038{col 54}{space 4} .0225805{col 67}{space 3} .8018296
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.816393{col 26}{space 2} .1992772{col 37}{space 1}   -9.11{col 46}{space 3}0.000{col 54}{space 4}-2.206969{col 67}{space 3}-1.425817
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.313574{col 26}{space 2} .2263464{col 37}{space 1}    5.80{col 46}{space 3}0.000{col 54}{space 4} .8699436{col 67}{space 3} 1.757205
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.089071{col 26}{space 2} .2107969{col 37}{space 1}    9.91{col 46}{space 3}0.000{col 54}{space 4} 1.675917{col 67}{space 3} 2.502225
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} -.014333{col 26}{space 2} .2246866{col 37}{space 1}   -0.06{col 46}{space 3}0.949{col 54}{space 4}-.4547106{col 67}{space 3} .4260446
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.183857{col 26}{space 2} .3011354{col 37}{space 1}    3.93{col 46}{space 3}0.000{col 54}{space 4} .5936422{col 67}{space 3} 1.774071
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7150227{col 26}{space 2} .2260556{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .2719619{col 67}{space 3} 1.158084
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6580175{col 26}{space 2} .1574948{col 37}{space 1}   -4.18{col 46}{space 3}0.000{col 54}{space 4}-.9667016{col 67}{space 3}-.3493335
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .8843444{col 26}{space 2} .1848832{col 37}{space 1}    4.78{col 46}{space 3}0.000{col 54}{space 4} .5219801{col 67}{space 3} 1.246709
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6058279{col 26}{space 2} .1229114{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .3649259{col 67}{space 3} .8467299
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2565824{col 26}{space 2}  .224882{col 37}{space 1}    1.14{col 46}{space 3}0.254{col 54}{space 4}-.1841784{col 67}{space 3} .6973431
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.825699{col 26}{space 2} .1630494{col 37}{space 1}   11.20{col 46}{space 3}0.000{col 54}{space 4} 1.506128{col 67}{space 3}  2.14527
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .7902075{col 26}{space 2} .1601998{col 37}{space 1}    4.93{col 46}{space 3}0.000{col 54}{space 4} .4762217{col 67}{space 3} 1.104193
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6753564{col 26}{space 2} .1533177{col 37}{space 1}   -4.40{col 46}{space 3}0.000{col 54}{space 4}-.9758536{col 67}{space 3}-.3748592
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2}  1.37583{col 26}{space 2} .2620995{col 37}{space 1}    5.25{col 46}{space 3}0.000{col 54}{space 4} .8621239{col 67}{space 3} 1.889535
{txt}{space 10}d3 {c |}{col 14}{res}{space 2}  1.73174{col 26}{space 2} .2401391{col 37}{space 1}    7.21{col 46}{space 3}0.000{col 54}{space 4} 1.261076{col 67}{space 3} 2.202404
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3939338{col 26}{space 2} .2471594{col 37}{space 1}    1.59{col 46}{space 3}0.111{col 54}{space 4}-.0904897{col 67}{space 3} .8783573
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.228652{col 26}{space 2} .3778378{col 37}{space 1}    5.90{col 46}{space 3}0.000{col 54}{space 4} 1.488103{col 67}{space 3}   2.9692
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.316159{col 26}{space 2} .2745437{col 37}{space 1}    4.79{col 46}{space 3}0.000{col 54}{space 4} .7780634{col 67}{space 3} 1.854255
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4777184{col 26}{space 2} .1578222{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-.7870443{col 67}{space 3}-.1683925
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.277826{col 26}{space 2} .2359986{col 37}{space 1}    5.41{col 46}{space 3}0.000{col 54}{space 4} .8152769{col 67}{space 3} 1.740374
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5824244{col 26}{space 2} .1600571{col 37}{space 1}    3.64{col 46}{space 3}0.000{col 54}{space 4} .2687183{col 67}{space 3} .8961304
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .6132958{col 26}{space 2} .2527468{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .1179211{col 67}{space 3} 1.108671
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.012506{col 26}{space 2} .1770152{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .6655625{col 67}{space 3}  1.35945
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4918598{col 26}{space 2} .1837335{col 37}{space 1}    2.68{col 46}{space 3}0.007{col 54}{space 4} .1317488{col 67}{space 3} .8519708
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.852933{col 26}{space 2} .1894807{col 37}{space 1}    9.78{col 46}{space 3}0.000{col 54}{space 4} 1.481558{col 67}{space 3} 2.224309
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.05515903 (.01324905)
{txt}    gw_know: {res}.08057961 (.03812818)
{txt}    pluralism: {res}.29518502 (.12213767)
{txt}    educ_plural: {res}-.152639 (.03409789)
{txt}    democrat: {res}-.09887151 (.02384166)
{txt}    republican: {res}.0194348 (.03580311)
{txt}    rep_educ_plural: {res}-.04016876 (.02766018)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.15676583 (.03489763)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.418091 (.2619363)
{txt}    d3: {res}2.0095888 (.22249104)
{txt}    d4: {res}2.4854329 (.27454854)
{txt}    d5: {res}3.1160159 (.35577716)
{txt}    d6: {res}2.4921799 (.27943337)
{txt}    d7: {res}1.5470493 (.18280998)
{txt}    d8: {res}2.0285523 (.23067473)
{txt}    d9: {res}1.0453767 (.13780425)
{txt}    d10: {res}2.495913 (.27601408)
{txt}    d11: {res}1.3836873 (.16292386)
{txt}    d12: {res}1.7961799 (.20071508)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.01557437 (.04030329)
{txt}    gender: {res}-.02886456 (.02972648)
{txt}    education: {res}.03892654 (.01495751)
{txt}    ideo: {res}-.03140866 (.01093252)
{txt}    pid: {res}.03162098 (.02327588)
{txt}    risk: {res}.68365503 (.10910133)
{txt}    nep: {res}.92308018 (.14259759)
{txt}    economy: {res}-.53878732 (.09056838)
{txt}    network: {res}.11040406 (.05000904)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix g = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,64]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .87163078     1.1580472     .04017246     .63236275    -.07007917     -1.155213     .19030728     .06433913

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.43400043     1.3105863     .41220505    -1.8163931     1.3135745     2.0890711    -.01433303     1.1838567

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .71502272    -.65801754     .88434437     .60582791     .25658236     1.8256993     .79020745    -.67535639

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.3758295     1.7317403     .39393377     2.2286515     1.3161592    -.47771843     1.2778255     .58242436

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism   educ_plural
y1 {res}    .61329584      1.012506     .49185977     1.8529335     .05515903     .08057961     .29518502      -.152639

{txt}            lns1:         lns1:         lns1:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
        democrat    republican  rep_educ_p~l            d2            d3            d4            d5            d6
y1 {res}   -.09887151      .0194348    -.04016876      2.418091     2.0095888     2.4854329     3.1160159     2.4921799

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:
              d7            d8            d9           d10           d11           d12            d1          race
y1 {res}    1.5470493     2.0285523     1.0453767      2.495913     1.3836873     1.7961799     .39593665    -.01557437

{txt}              f1:           f1:           f1:           f1:           f1:           f1:           f1:           f1:
          gender     education          ideo           pid          risk           nep       economy       network
y1 {res}   -.02886456     .03892654    -.03140866     .03162098     .68365503     .92308018    -.53878732     .11040406
{reset}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPS_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Jul 2016, 13:22:31
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPS_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Jul 2016, 12:34:31
{txt}
{com}. 
. use "/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/noaasammy.dta"
{txt}
{com}. 
. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPS_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Jul 2016, 12:34:42
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPS_logfile.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Jul 2016, 14:09:26
{txt}
{com}. 
{txt}end of do-file

{com}. /**** Estimate without risk perceptions or identity variables 
{err}unrecognized command:  / invalid command name
{txt}{search r(199):r(199);}

{com}. eq f1: education ideo pid nep economy network  

. 
. eq het: education gw_know pluralism educ_plural

. 
. ** estimate structural and variance equation 

. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}{err}initial vector: extra parameter f1:race found
specify skip option if necessary

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,60]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}   .7624484   1.0094724   .03517484   .56115757  -.05016161  -1.0031581   .18565151   .06461104  -.38292097

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.1269265   .35988791  -1.5727731    1.151419   1.8286288  -.00792482   1.0280859   .62352228  -.57822448

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .77379097   .53790042   .21316156   1.5960047   .68434488  -.59407843   1.2142793   1.5263236    .3382102

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  1.9187868   1.1208735  -.43854418    1.114969   .52030043   .50441252   .89111953   .42051451   1.6120337

{txt}          lns1:       lns1:       lns1:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
     education     gw_know   pluralism          d2          d3          d4          d5          d6          d7
y1 {res}  .00744834   .08562931   -.2421856   2.4299534   2.0161188   2.4835803   3.0909199    2.462216   1.5304397

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:
            d8          d9         d10         d11         d12          d1        race      gender   education
y1 {res}  2.0278793   1.0559624   2.4671228   1.3841033   1.7845717   .34542111  -.01252856   -.0244732   .03242503

{txt}            f1:         f1:         f1:         f1:         f1:         f1:
          ideo         pid        risk         nep     economy     network
y1 {res} -.02742796   .03048737   .59311667   .80454044  -.46702143   .09726415
{reset}
{com}. 
. ** estimate structural and variance equation 

. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) adapt 
{res}
{txt}Running adaptive quadrature
{err}Convergence not achieved: try with more quadrature points

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,36]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .35637045   .60541814  -.11670047   .27891635   -.1642752  -.87211588  -.01367938   .02690743  -.32372333

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  .84785415   .15373054  -2.0085784   .42699861   1.0658867  -.38624349   .20320461    .0621525  -.68621623

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .25197489   .49650498  -.23847518   1.2238569   .29292391  -1.1423829  -.07191549   .42409521  -.64587999

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  -.0486106  -.14231045  -.72180901   .11992865   .42510431  -.53794512   .43384897  -.19660634   .85398983
{reset}
{com}. 
. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. ** estimate structural model (no het) 
. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-10762.972
{txt}Iteration 1:    log likelihood = {res}-10569.409
{txt}Iteration 2:    log likelihood = {res}-10484.975
{txt}Iteration 3:    log likelihood = {res}-10035.914
{txt}Iteration 4:    log likelihood = {res}-10020.503
{txt}Iteration 5:    log likelihood = {res} -10014.68
{txt}Iteration 6:    log likelihood = {res} -10001.45
{txt}Iteration 7:    log likelihood = {res}-10000.853
{txt}Iteration 8:    log likelihood = {res}-9999.3455
{txt}Iteration 9:    log likelihood = {res}-9999.3032
{txt}Iteration 10:    log likelihood = {res} -9999.113
{txt}Iteration 11:    log likelihood = {res}-9999.1018
{txt}Iteration 12:    log likelihood = {res}-9999.1018


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9999.1018{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9999.1018{txt}  
Iteration 2:{col 16}log likelihood = {res}-9997.1721{txt}  
Iteration 3:{col 16}log likelihood = {res}-9996.3969{txt}  
Iteration 4:{col 16}log likelihood = {res}-9996.3868{txt}  
Iteration 5:{col 16}log likelihood = {res}-9996.3868{txt}  
{res} 
{txt}number of level 1 units = {res}11825
{txt}number of level 2 units = {res}1025
 
{txt}Condition Number = {res}395.39516
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9996.3868
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .4345583{col 26}{space 2} .1967833{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0488701{col 67}{space 3} .8202466
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} .7843812{col 26}{space 2} .1579706{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .4747645{col 67}{space 3} 1.093998
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.3332965{col 26}{space 2} .2199172{col 37}{space 1}   -1.52{col 46}{space 3}0.130{col 54}{space 4}-.7643262{col 67}{space 3} .0977333
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .1119052{col 26}{space 2} .2657906{col 37}{space 1}    0.42{col 46}{space 3}0.674{col 54}{space 4}-.4090347{col 67}{space 3} .6328452
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.4239747{col 26}{space 2}  .227538{col 37}{space 1}   -1.86{col 46}{space 3}0.062{col 54}{space 4}-.8699411{col 67}{space 3} .0219917
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.189875{col 26}{space 2} .3303878{col 37}{space 1}   -3.60{col 46}{space 3}0.000{col 54}{space 4}-1.837423{col 67}{space 3}-.5423273
{txt}{space 10}d8 {c |}{col 14}{res}{space 2}-.0808235{col 26}{space 2} .1851957{col 37}{space 1}   -0.44{col 46}{space 3}0.663{col 54}{space 4}-.4438005{col 67}{space 3} .2821534
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0546771{col 26}{space 2} .1374334{col 37}{space 1}    0.40{col 46}{space 3}0.691{col 54}{space 4}-.2146874{col 67}{space 3} .3240416
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.7444335{col 26}{space 2} .2460884{col 37}{space 1}   -3.03{col 46}{space 3}0.002{col 54}{space 4}-1.226758{col 67}{space 3}-.2621091
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.038443{col 26}{space 2} .1229738{col 37}{space 1}    8.44{col 46}{space 3}0.000{col 54}{space 4}  .797419{col 67}{space 3} 1.279467
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .1930795{col 26}{space 2} .1558409{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.1123631{col 67}{space 3} .4985221
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.760605{col 26}{space 2} .1369265{col 37}{space 1}  -12.86{col 46}{space 3}0.000{col 54}{space 4}-2.028976{col 67}{space 3}-1.492234
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8650127{col 26}{space 2} .1703316{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .5311689{col 67}{space 3} 1.198856
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.601055{col 26}{space 2} .1352386{col 37}{space 1}   11.84{col 46}{space 3}0.000{col 54}{space 4} 1.335993{col 67}{space 3} 1.866118
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.3659499{col 26}{space 2}  .174931{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.7088082{col 67}{space 3}-.0230915
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .6198661{col 26}{space 2} .2387339{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .1519562{col 67}{space 3} 1.087776
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .3274948{col 26}{space 2} .1701417{col 37}{space 1}    1.92{col 46}{space 3}0.054{col 54}{space 4}-.0059767{col 67}{space 3} .6609664
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.7125904{col 26}{space 2} .1213111{col 37}{space 1}   -5.87{col 46}{space 3}0.000{col 54}{space 4}-.9503557{col 67}{space 3}-.4748251
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .5324579{col 26}{space 2} .1342162{col 37}{space 1}    3.97{col 46}{space 3}0.000{col 54}{space 4}  .269399{col 67}{space 3} .7955168
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5351156{col 26}{space 2} .0828539{col 37}{space 1}    6.46{col 46}{space 3}0.000{col 54}{space 4}  .372725{col 67}{space 3} .6975062
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.1308547{col 26}{space 2} .1706731{col 37}{space 1}   -0.77{col 46}{space 3}0.443{col 54}{space 4}-.4653678{col 67}{space 3} .2036583
{txt}{space 9}d11 {c |}{col 14}{res}{space 2}  1.52323{col 26}{space 2} .0958587{col 37}{space 1}   15.89{col 46}{space 3}0.000{col 54}{space 4}  1.33535{col 67}{space 3} 1.711109
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .520642{col 26}{space 2} .1151105{col 37}{space 1}    4.52{col 46}{space 3}0.000{col 54}{space 4} .2950295{col 67}{space 3} .7462544
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7988047{col 26}{space 2} .1131875{col 37}{space 1}   -7.06{col 46}{space 3}0.000{col 54}{space 4}-1.020648{col 67}{space 3}-.5769613
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .9358563{col 26}{space 2} .1935633{col 37}{space 1}    4.83{col 46}{space 3}0.000{col 54}{space 4} .5564793{col 67}{space 3} 1.315233
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.294697{col 26}{space 2} .1656375{col 37}{space 1}    7.82{col 46}{space 3}0.000{col 54}{space 4} .9700536{col 67}{space 3} 1.619341
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0259183{col 26}{space 2} .1803492{col 37}{space 1}    0.14{col 46}{space 3}0.886{col 54}{space 4}-.3275597{col 67}{space 3} .3793962
{txt}{space 10}d5 {c |}{col 14}{res}{space 2}  1.54936{col 26}{space 2} .2809802{col 37}{space 1}    5.51{col 46}{space 3}0.000{col 54}{space 4} .9986488{col 67}{space 3} 2.100071
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7981068{col 26}{space 2} .1936634{col 37}{space 1}    4.12{col 46}{space 3}0.000{col 54}{space 4} .4185334{col 67}{space 3}  1.17768
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5207152{col 26}{space 2} .1124634{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4}-.7411394{col 67}{space 3}-.3002909
{txt}{space 10}d8 {c |}{col 14}{res}{space 2}  .895885{col 26}{space 2}  .164874{col 37}{space 1}    5.43{col 46}{space 3}0.000{col 54}{space 4} .5727379{col 67}{space 3} 1.219032
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .4873555{col 26}{space 2} .1101302{col 37}{space 1}    4.43{col 46}{space 3}0.000{col 54}{space 4} .2715043{col 67}{space 3} .7032068
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .1681512{col 26}{space 2} .1801716{col 37}{space 1}    0.93{col 46}{space 3}0.351{col 54}{space 4}-.1849785{col 67}{space 3}  .521281
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .8150809{col 26}{space 2} .1226954{col 37}{space 1}    6.64{col 46}{space 3}0.000{col 54}{space 4} .5746022{col 67}{space 3}  1.05556
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .2685699{col 26}{space 2} .1320078{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4} .0098395{col 67}{space 3} .5273004
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.380279{col 26}{space 2} .1192334{col 37}{space 1}   11.58{col 46}{space 3}0.000{col 54}{space 4} 1.146585{col 67}{space 3} 1.613972
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.11993713 (.02356041)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.5384738 (.27198065)
{txt}    d3: {res}2.0689998 (.22646397)
{txt}    d4: {res}2.5025928 (.27428197)
{txt}    d5: {res}3.3008433 (.37415003)
{txt}    d6: {res}2.52617 (.28072484)
{txt}    d7: {res}1.6129875 (.1881854)
{txt}    d8: {res}2.073621 (.23396554)
{txt}    d9: {res}1.0279521 (.13573481)
{txt}    d10: {res}2.4886986 (.27317023)
{txt}    d11: {res}1.4558727 (.16845823)
{txt}    d12: {res}1.8297463 (.2028004)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}6{txt} covariates:
    education: {res}.01815855 (.01217393)
{txt}    ideo: {res}-.03691716 (.00938116)
{txt}    pid: {res}.05211897 (.01988021)
{txt}    nep: {res}1.0290404 (.12535081)
{txt}    economy: {res}-.4707479 (.07371085)
{txt}    network: {res}.11817199 (.04206681)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,54]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .43455834   .78438123  -.33329646   .11190522   -.4239747  -1.1898754  -.08082352   .05467711  -.74443348

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.0384431   .19307946  -1.7606049   .86501268   1.6010552  -.36594985   .61986609   .32749484  -.71259038

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .53245788   .53511562  -.13085473   1.5232296   .52064198  -.79880467   .93585628   1.2946972   .02591828

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}    1.54936    .7981068  -.52071517   .89588501   .48735554   .16815124   .81508087   .26856992   1.3802787

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  2.5384738   2.0689998   2.5025928   3.3008433     2.52617   1.6129875    2.073621   1.0279521   2.4886986

{txt}        id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:         f1:         f1:         f1:
           d11         d12          d1   education        ideo         pid         nep     economy     network
y1 {res}  1.4558727   1.8297463   .34631941   .01815855  -.03691716   .05211897   1.0290404   -.4707479   .11817199
{reset}
{com}. 
{txt}end of do-file

{com}. matrix a=e(b)

. matrix list e(b)gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(a) adapt 
{err}option i() not allowed
{txt}{search r(198):r(198);}

{com}. matrix b = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,54]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .43455834   .78438123  -.33329646   .11190522   -.4239747  -1.1898754  -.08082352   .05467711  -.74443348

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.0384431   .19307946  -1.7606049   .86501268   1.6010552  -.36594985   .61986609   .32749484  -.71259038

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .53245788   .53511562  -.13085473   1.5232296   .52064198  -.79880467   .93585628   1.2946972   .02591828

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}    1.54936    .7981068  -.52071517   .89588501   .48735554   .16815124   .81508087   .26856992   1.3802787

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  2.5384738   2.0689998   2.5025928   3.3008433     2.52617   1.6129875    2.073621   1.0279521   2.4886986

{txt}        id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:         f1:         f1:         f1:
           d11         d12          d1   education        ideo         pid         nep     economy     network
y1 {res}  1.4558727   1.8297463   .34631941   .01815855  -.03691716   .05211897   1.0290404   -.4707479   .11817199
{reset}
{com}. eq het: education gw_know pluralism gwknow_plural

. 
. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9981.6571
{txt}Iteration 1:    log likelihood = {res} -9958.189
{txt}Iteration 2:    log likelihood = {res} -9953.846
{txt}Iteration 3:    log likelihood = {res} -9953.846


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -9953.846{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res} -9953.846{txt}  
Iteration 2:{col 16}log likelihood = {res}-9952.4411{txt}  
Iteration 3:{col 16}log likelihood = {res}-9951.8277{txt}  
Iteration 4:{col 16}log likelihood = {res}-9951.8029{txt}  
Iteration 5:{col 16}log likelihood = {res}-9951.8022{txt}  
{res} 
{txt}number of level 1 units = {res}11802
{txt}number of level 2 units = {res}1023
 
{txt}Condition Number = {res}375.39469
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9951.8022
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .4316315{col 26}{space 2} .1883862{col 37}{space 1}    2.29{col 46}{space 3}0.022{col 54}{space 4} .0624013{col 67}{space 3} .8008617
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} .7505023{col 26}{space 2} .1539166{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .4488312{col 67}{space 3} 1.052173
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2713415{col 26}{space 2} .2116384{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-.6861451{col 67}{space 3} .1434622
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .1302213{col 26}{space 2}  .252158{col 37}{space 1}    0.52{col 46}{space 3}0.606{col 54}{space 4}-.3639993{col 67}{space 3} .6244419
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.3766335{col 26}{space 2} .2162679{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.8005109{col 67}{space 3} .0472439
{txt}{space 10}d7 {c |}{col 14}{res}{space 2} -1.11988{col 26}{space 2} .3187033{col 37}{space 1}   -3.51{col 46}{space 3}0.000{col 54}{space 4}-1.744527{col 67}{space 3}-.4952327
{txt}{space 10}d8 {c |}{col 14}{res}{space 2}-.0673418{col 26}{space 2} .1758955{col 37}{space 1}   -0.38{col 46}{space 3}0.702{col 54}{space 4}-.4120906{col 67}{space 3}  .277407
{txt}{space 10}d9 {c |}{col 14}{res}{space 2}  .003807{col 26}{space 2} .1346167{col 37}{space 1}    0.03{col 46}{space 3}0.977{col 54}{space 4}-.2600369{col 67}{space 3}  .267651
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.6899547{col 26}{space 2} .2375423{col 37}{space 1}   -2.90{col 46}{space 3}0.004{col 54}{space 4}-1.155529{col 67}{space 3}-.2243804
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9924461{col 26}{space 2} .1232301{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} .7509195{col 67}{space 3} 1.233973
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .1729178{col 26}{space 2} .1492535{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4}-.1196136{col 67}{space 3} .4654492
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.693201{col 26}{space 2} .1510153{col 37}{space 1}  -11.21{col 46}{space 3}0.000{col 54}{space 4}-1.989185{col 67}{space 3}-1.397216
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8262821{col 26}{space 2} .1652642{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .5023702{col 67}{space 3} 1.150194
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.524491{col 26}{space 2} .1436022{col 37}{space 1}   10.62{col 46}{space 3}0.000{col 54}{space 4} 1.243036{col 67}{space 3} 1.805946
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.3237677{col 26}{space 2} .1691009{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.6551993{col 67}{space 3}  .007664
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .5854516{col 26}{space 2}  .227612{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .1393403{col 67}{space 3} 1.031563
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .2978028{col 26}{space 2} .1621648{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0200344{col 67}{space 3} .6156401
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6683266{col 26}{space 2} .1183705{col 37}{space 1}   -5.65{col 46}{space 3}0.000{col 54}{space 4}-.9003284{col 67}{space 3}-.4363247
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .4938387{col 26}{space 2}  .128408{col 37}{space 1}    3.85{col 46}{space 3}0.000{col 54}{space 4} .2421637{col 67}{space 3} .7455137
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5034785{col 26}{space 2} .0816931{col 37}{space 1}    6.16{col 46}{space 3}0.000{col 54}{space 4}  .343363{col 67}{space 3}  .663594
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.1219912{col 26}{space 2} .1613455{col 37}{space 1}   -0.76{col 46}{space 3}0.450{col 54}{space 4}-.4382225{col 67}{space 3} .1942402
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.442172{col 26}{space 2} .1075564{col 37}{space 1}   13.41{col 46}{space 3}0.000{col 54}{space 4} 1.231365{col 67}{space 3} 1.652978
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4893508{col 26}{space 2} .1101795{col 37}{space 1}    4.44{col 46}{space 3}0.000{col 54}{space 4}  .273403{col 67}{space 3} .7052986
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7634599{col 26}{space 2}  .115947{col 37}{space 1}   -6.58{col 46}{space 3}0.000{col 54}{space 4}-.9907118{col 67}{space 3} -.536208
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .8905115{col 26}{space 2} .1883129{col 37}{space 1}    4.73{col 46}{space 3}0.000{col 54}{space 4}  .521425{col 67}{space 3} 1.259598
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.251085{col 26}{space 2} .1670814{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .9236118{col 67}{space 3} 1.578559
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0219467{col 26}{space 2} .1747354{col 37}{space 1}    0.13{col 46}{space 3}0.900{col 54}{space 4}-.3205283{col 67}{space 3} .3644217
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.477344{col 26}{space 2} .2748858{col 37}{space 1}    5.37{col 46}{space 3}0.000{col 54}{space 4} .9385782{col 67}{space 3} 2.016111
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .7526811{col 26}{space 2}  .188303{col 37}{space 1}    4.00{col 46}{space 3}0.000{col 54}{space 4} .3836139{col 67}{space 3} 1.121748
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5097579{col 26}{space 2} .1090487{col 37}{space 1}   -4.67{col 46}{space 3}0.000{col 54}{space 4}-.7234895{col 67}{space 3}-.2960263
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .8495438{col 26}{space 2} .1613804{col 37}{space 1}    5.26{col 46}{space 3}0.000{col 54}{space 4} .5332441{col 67}{space 3} 1.165844
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .4833584{col 26}{space 2} .1088209{col 37}{space 1}    4.44{col 46}{space 3}0.000{col 54}{space 4} .2700734{col 67}{space 3} .6966434
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .1332581{col 26}{space 2}  .171053{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.2019997{col 67}{space 3} .4685158
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .7771725{col 26}{space 2} .1207872{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4} .5404339{col 67}{space 3} 1.013911
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .234931{col 26}{space 2} .1251857{col 37}{space 1}    1.88{col 46}{space 3}0.061{col 54}{space 4}-.0104284{col 67}{space 3} .4802905
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.340594{col 26}{space 2} .1326265{col 37}{space 1}   10.11{col 46}{space 3}0.000{col 54}{space 4} 1.080651{col 67}{space 3} 1.600537
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.00628188 (.00933602)
{txt}    gw_know: {res}.00535861 (.05336067)
{txt}    pluralism: {res}-.39025382 (.07655191)
{txt}    gwknow_plural: {res}.29477773 (.14165907)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.12060301 (.02552384)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4471648 (.25845683)
{txt}    d3: {res}2.0071168 (.21714766)
{txt}    d4: {res}2.4553526 (.26488288)
{txt}    d5: {res}3.167078 (.35454114)
{txt}    d6: {res}2.4411827 (.26806968)
{txt}    d7: {res}1.5705316 (.18062271)
{txt}    d8: {res}1.9982083 (.2228797)
{txt}    d9: {res}1.0396784 (.13427382)
{txt}    d10: {res}2.3988106 (.25965482)
{txt}    d11: {res}1.3775297 (.15888556)
{txt}    d12: {res}1.7633675 (.19309087)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}6{txt} covariates:
    education: {res}.01908389 (.01230342)
{txt}    ideo: {res}-.03687175 (.00949823)
{txt}    pid: {res}.05181335 (.02000278)
{txt}    nep: {res}1.0351533 (.13243689)
{txt}    economy: {res}-.48853712 (.0772308)
{txt}    network: {res}.12059826 (.04258689)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. eq het: education gw_know pluralism educ_plural
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. matrix c = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,58]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .43163148     .75050233    -.27134146      .1302213     -.3766335    -1.1198796    -.06734181     .00380701

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.68995468     .99244609      .1729178    -1.6932007      .8262821     1.5244906    -.32376767     .58545163

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .29780284    -.66832657     .49383866     .50347849    -.12199116     1.4421718     .48935082    -.76345992

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .89051145     1.2510854      .0219467     1.4773445     .75268106    -.50975791     .84954383     .48335838

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism  gwknow_plu~l
y1 {res}    .13325807      .7771725     .23493103     1.3405938     .00628188     .00535861    -.39025382     .29477773

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    2.4471648     2.0071168     2.4553526      3.167078     2.4411827     1.5705316     1.9982083     1.0396784

{txt}          id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:           f1:
             d10           d11           d12            d1     education          ideo           pid           nep
y1 {res}    2.3988106     1.3775297     1.7633675     .34727944     .01908389    -.03687175     .05181335     1.0351533

{txt}              f1:           f1:
         economy       network
y1 {res}   -.48853712     .12059826
{reset}
{com}. 
{txt}end of do-file

{com}. ** estimate structural and variance equation 

. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(c) adapt 
{res}{err}initial vector: extra parameter lns1:gwknow_plural found
specify skip option if necessary

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,58]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .43163148     .75050233    -.27134146      .1302213     -.3766335    -1.1198796    -.06734181     .00380701

{txt}          _cut11:       _cut11:       _cut11:       _cut11:       _cut12:       _cut12:       _cut12:       _cut12:
             d10           d11           d12         _cons            d2            d3            d4            d5
y1 {res}   -.68995468     .99244609      .1729178    -1.6932007      .8262821     1.5244906    -.32376767     .58545163

{txt}          _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:       _cut12:
              d6            d7            d8            d9           d10           d11           d12         _cons
y1 {res}    .29780284    -.66832657     .49383866     .50347849    -.12199116     1.4421718     .48935082    -.76345992

{txt}          _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:       _cut13:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .89051145     1.2510854      .0219467     1.4773445     .75268106    -.50975791     .84954383     .48335838

{txt}          _cut13:       _cut13:       _cut13:       _cut13:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism  gwknow_plu~l
y1 {res}    .13325807      .7771725     .23493103     1.3405938     .00628188     .00535861    -.39025382     .29477773

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    2.4471648     2.0071168     2.4553526      3.167078     2.4411827     1.5705316     1.9982083     1.0396784

{txt}          id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:           f1:
             d10           d11           d12            d1     education          ideo           pid           nep
y1 {res}    2.3988106     1.3775297     1.7633675     .34727944     .01908389    -.03687175     .05181335     1.0351533

{txt}              f1:           f1:
         economy       network
y1 {res}   -.48853712     .12059826
{reset}
{com}. ** estimate structural and variance equation 

. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-9981.6571
{txt}Iteration 1:    log likelihood = {res}-9948.6216
{txt}Iteration 2:    log likelihood = {res}-9948.6216


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9948.6216{txt}  
Iteration 1:{col 16}log likelihood = {res}-9948.6216{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-9945.3013{txt}  
Iteration 3:{col 16}log likelihood = {res}-9944.7824{txt}  
Iteration 4:{col 16}log likelihood = {res}-9944.7711{txt}  
Iteration 5:{col 16}log likelihood = {res}-9944.7711{txt}  
{res} 
{txt}number of level 1 units = {res}11802
{txt}number of level 2 units = {res}1023
 
{txt}Condition Number = {res}368.00299
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9944.7711
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .5284584{col 26}{space 2} .2240368{col 37}{space 1}    2.36{col 46}{space 3}0.018{col 54}{space 4} .0893543{col 67}{space 3} .9675625
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} .9078791{col 26}{space 2}  .184995{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4} .5452955{col 67}{space 3} 1.270463
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.3084109{col 26}{space 2}   .25075{col 37}{space 1}   -1.23{col 46}{space 3}0.219{col 54}{space 4}-.7998719{col 67}{space 3} .1830502
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .1883459{col 26}{space 2} .2968825{col 37}{space 1}    0.63{col 46}{space 3}0.526{col 54}{space 4}-.3935331{col 67}{space 3}  .770225
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.4238267{col 26}{space 2} .2568727{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.9272879{col 67}{space 3} .0796345
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.304229{col 26}{space 2} .3791277{col 37}{space 1}   -3.44{col 46}{space 3}0.001{col 54}{space 4}-2.047306{col 67}{space 3}-.5611525
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} -.060885{col 26}{space 2} .2088757{col 37}{space 1}   -0.29{col 46}{space 3}0.771{col 54}{space 4}-.4702738{col 67}{space 3} .3485038
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .0205087{col 26}{space 2} .1596561{col 37}{space 1}    0.13{col 46}{space 3}0.898{col 54}{space 4}-.2924114{col 67}{space 3} .3334288
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.7949084{col 26}{space 2} .2828943{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-1.349371{col 67}{space 3}-.2404458
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.197582{col 26}{space 2} .1511883{col 37}{space 1}    7.92{col 46}{space 3}0.000{col 54}{space 4} .9012589{col 67}{space 3} 1.493906
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .2190908{col 26}{space 2} .1775878{col 37}{space 1}    1.23{col 46}{space 3}0.217{col 54}{space 4}-.1289748{col 67}{space 3} .5671565
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.008627{col 26}{space 2} .1867983{col 37}{space 1}  -10.75{col 46}{space 3}0.000{col 54}{space 4}-2.374745{col 67}{space 3}-1.642509
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .9880394{col 26}{space 2} .1979804{col 37}{space 1}    4.99{col 46}{space 3}0.000{col 54}{space 4} .6000049{col 67}{space 3} 1.376074
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.814565{col 26}{space 2}  .177197{col 37}{space 1}   10.24{col 46}{space 3}0.000{col 54}{space 4} 1.467265{col 67}{space 3} 2.161864
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.3716594{col 26}{space 2} .1999731{col 37}{space 1}   -1.86{col 46}{space 3}0.063{col 54}{space 4}-.7635995{col 67}{space 3} .0202807
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .7138579{col 26}{space 2} .2687435{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .1871302{col 67}{space 3} 1.240586
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .3686185{col 26}{space 2}  .193886{col 37}{space 1}    1.90{col 46}{space 3}0.057{col 54}{space 4} -.011391{col 67}{space 3}  .748628
{txt}{space 10}d7 {c |}{col 14}{res}{space 2} -.779255{col 26}{space 2} .1407474{col 37}{space 1}   -5.54{col 46}{space 3}0.000{col 54}{space 4}-1.055115{col 67}{space 3}-.5033952
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .5993747{col 26}{space 2} .1537346{col 37}{space 1}    3.90{col 46}{space 3}0.000{col 54}{space 4} .2980603{col 67}{space 3}  .900689
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6017576{col 26}{space 2} .0983884{col 37}{space 1}    6.12{col 46}{space 3}0.000{col 54}{space 4} .4089199{col 67}{space 3} .7945953
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.1259885{col 26}{space 2}  .192273{col 37}{space 1}   -0.66{col 46}{space 3}0.512{col 54}{space 4}-.5028366{col 67}{space 3} .2508597
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.717081{col 26}{space 2} .1359806{col 37}{space 1}   12.63{col 46}{space 3}0.000{col 54}{space 4} 1.450564{col 67}{space 3} 1.983598
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .5887915{col 26}{space 2} .1320342{col 37}{space 1}    4.46{col 46}{space 3}0.000{col 54}{space 4} .3300093{col 67}{space 3} .8475738
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9015494{col 26}{space 2} .1389781{col 37}{space 1}   -6.49{col 46}{space 3}0.000{col 54}{space 4}-1.173941{col 67}{space 3}-.6291574
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.059766{col 26}{space 2} .2252269{col 37}{space 1}    4.71{col 46}{space 3}0.000{col 54}{space 4} .6183293{col 67}{space 3} 1.501202
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.489404{col 26}{space 2} .2025996{col 37}{space 1}    7.35{col 46}{space 3}0.000{col 54}{space 4} 1.092316{col 67}{space 3} 1.886492
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .0359586{col 26}{space 2} .2068316{col 37}{space 1}    0.17{col 46}{space 3}0.862{col 54}{space 4}-.3694239{col 67}{space 3} .4413412
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.761585{col 26}{space 2} .3286996{col 37}{space 1}    5.36{col 46}{space 3}0.000{col 54}{space 4} 1.117346{col 67}{space 3} 2.405824
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .9151416{col 26}{space 2} .2273297{col 37}{space 1}    4.03{col 46}{space 3}0.000{col 54}{space 4} .4695835{col 67}{space 3}   1.3607
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5988753{col 26}{space 2} .1294606{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4}-.8526134{col 67}{space 3}-.3451372
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.021641{col 26}{space 2} .1948491{col 37}{space 1}    5.24{col 46}{space 3}0.000{col 54}{space 4} .6397437{col 67}{space 3} 1.403538
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5803862{col 26}{space 2} .1303611{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} .3248831{col 67}{space 3} .8358892
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .1778771{col 26}{space 2} .2043599{col 37}{space 1}    0.87{col 46}{space 3}0.384{col 54}{space 4} -.222661{col 67}{space 3} .5784152
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9287914{col 26}{space 2} .1462116{col 37}{space 1}    6.35{col 46}{space 3}0.000{col 54}{space 4}  .642222{col 67}{space 3} 1.215361
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .284826{col 26}{space 2} .1490285{col 37}{space 1}    1.91{col 46}{space 3}0.056{col 54}{space 4}-.0072645{col 67}{space 3} .5769165
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.588183{col 26}{space 2} .1622059{col 37}{space 1}    9.79{col 46}{space 3}0.000{col 54}{space 4} 1.270265{col 67}{space 3}   1.9061
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.04265217 (.01275418)
{txt}    gw_know: {res}.07379599 (.03743875)
{txt}    pluralism: {res}.23778251 (.12023803)
{txt}    educ_plural: {res}-.14167775 (.03314227)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.16887511 (.03661153)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.4501297 (.25901194)
{txt}    d3: {res}2.0089901 (.21743253)
{txt}    d4: {res}2.4530332 (.26489472)
{txt}    d5: {res}3.1516127 (.35292503)
{txt}    d6: {res}2.456176 (.26980957)
{txt}    d7: {res}1.561906 (.17997195)
{txt}    d8: {res}2.0033411 (.22346053)
{txt}    d9: {res}1.0380458 (.13412901)
{txt}    d10: {res}2.4136041 (.26132486)
{txt}    d11: {res}1.3823739 (.15934385)
{txt}    d12: {res}1.7668195 (.1935688)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}6{txt} covariates:
    education: {res}.02436048 (.0146333)
{txt}    ideo: {res}-.04372803 (.01129496)
{txt}    pid: {res}.06184256 (.02373974)
{txt}    nep: {res}1.2272149 (.15981812)
{txt}    economy: {res}-.57851208 (.09253898)
{txt}    network: {res}.14143513 (.05038238)
{txt}------------------------------------------------------------------------------

{res} 

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,58]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}    .5284584    .90787905   -.30841085    .18834593   -.42382673   -1.3042292   -.06088503     .0205087

{txt}         _cut11:      _cut11:      _cut11:      _cut11:      _cut12:      _cut12:      _cut12:      _cut12:
            d10          d11          d12        _cons           d2           d3           d4           d5
y1 {res}   -.7949084    1.1975825    .21909084   -2.0086268    .98803938    1.8145645   -.37165941    .71385789

{txt}         _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:      _cut12:
             d6           d7           d8           d9          d10          d11          d12        _cons
y1 {res}    .3686185   -.77925504    .59937465    .60175761   -.12598848    1.7170814    .58879153   -.90154936

{txt}         _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:      _cut13:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.0597658    1.4894042    .03595864    1.7615851     .9151416   -.59887533    1.0216409    .58038617

{txt}         _cut13:      _cut13:      _cut13:      _cut13:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .17787707    .92879142    .28482596    1.5881826    .04265217    .07379599    .23778251   -.14167775

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   2.4501297    2.0089901    2.4530332    3.1516127     2.456176     1.561906    2.0033411    1.0380458

{txt}         id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:          f1:          f1:
            d10          d11          d12           d1    education         ideo          pid          nep
y1 {res}   2.4136041    1.3823739    1.7668195    .41094417    .02436048   -.04372803    .06184256    1.2272149

{txt}             f1:          f1:
        economy      network
y1 {res}  -.57851208    .14143513
{reset}
{com}. eq f1: education ideo pid network  

. 
. gllamm y, i(id) l(soprob) f(binom) eqs(load) geqs(f1) s(het) adapt 
{res}
{txt}Running adaptive quadrature
{err}Convergence not achieved: try with more quadrature points

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,3]
        _cut11:     _cut12:     _cut13:
         _cons       _cons       _cons
y1 {res} -1.7936505  -.81362173   .71436057
{reset}
{com}. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}{err}initial vector: extra parameter f1:nep found
specify skip option if necessary

{com}. matrix d = e(b)

. matrix list e(b)
{res}
{txt}e(b)[1,3]
        _cut11:     _cut12:     _cut13:
         _cons       _cons       _cons
y1 {res} -1.7936505  -.81362173   .71436057
{reset}
{com}. gllamm y, i(id) l(soprob) f(binom) geqs(f1) s(het) adapt 
{res}
{txt}Running adaptive quadrature
{err}Convergence not achieved: try with more quadrature points

{com}. 
. gllamm y, i(id) l(oprob) f(binom) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-12065.931
{txt}Iteration 1:    log likelihood = {res}-11590.404
{txt}Iteration 2:    log likelihood = {res}-10992.087
{txt}Iteration 3:    log likelihood = {res}-10838.643
{txt}Iteration 4:    log likelihood = {res}-10824.881
{txt}Iteration 5:    log likelihood = {res}-10681.782
{txt}Iteration 6:    log likelihood = {res}-10662.597
{txt}Iteration 7:    log likelihood = {res}-10662.351
{txt}Iteration 8:    log likelihood = {res}-10662.351


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-10662.351{txt}  
Iteration 1:{col 16}log likelihood = {res}-10662.351{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-10662.336{txt}  
Iteration 3:{col 16}log likelihood = {res}-10662.336{txt}  
{res} 
{txt}number of level 1 units = {res}11894
{txt}number of level 2 units = {res}1033
 
{txt}Condition Number = {res}32.528038
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-10662.336
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2590421{col 26}{space 2} .0879851{col 37}{space 1}   -2.94{col 46}{space 3}0.003{col 54}{space 4}-.4314896{col 67}{space 3}-.0865945
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.043213{col 26}{space 2} .0899308{col 37}{space 1}   11.60{col 46}{space 3}0.000{col 54}{space 4} .8669517{col 67}{space 3} 1.219474
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.224706{col 26}{space 2} .0964151{col 37}{space 1}   33.45{col 46}{space 3}0.000{col 54}{space 4} 3.035736{col 67}{space 3} 3.413676
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.73907213 (.05177101)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}.98552127 (.02368427)
{txt}    d3: {res}.65815031 (.02160038)
{txt}    d4: {res}1.3972112 (.03561729)
{txt}    d5: {res}1.0334003 (.02507772)
{txt}    d6: {res}1.0941176 (.02586166)
{txt}    d7: {res}1.4397693 (.03650798)
{txt}    d8: {res}.95290201 (.02350067)
{txt}    d9: {res}.79326751 (.02267185)
{txt}    d10: {res}1.3221003 (.03274773)
{txt}    d11: {res}.56995842 (.02254816)
{txt}    d12: {res}1.0468807 (.02475816)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}4{txt} covariates:
    education: {res}.27299975 (.02500935)
{txt}    ideo: {res}.07135546 (.01514742)
{txt}    pid: {res}.5167044 (.04184786)
{txt}    network: {res}.67645738 (.0952167)
{txt}------------------------------------------------------------------------------

{res} 

{com}. 
. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,19]
        _cut11:     _cut12:     _cut13:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
         _cons       _cons       _cons          d2          d3          d4          d5          d6          d7
y1 {res} -.25904207   1.0432129   3.2247064   .98552127   .65815031   1.3972112   1.0334003   1.0941176   1.4397693

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:
            d8          d9         d10         d11         d12          d1   education        ideo         pid
y1 {res}  .95290201   .79326751   1.3221003   .56995842   1.0468807   .85969304   .27299975   .07135546    .5167044

{txt}            f1:
       network
y1 {res}  .67645738
{reset}
{com}. 
{txt}end of do-file

{com}. gllamm y, i(id) l(soprob) f(binom) eqs(load) geqs(f1) s(het) from(a) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-10581.742
{txt}Iteration 1:    log likelihood = {res} -10530.83
{txt}Iteration 2:    log likelihood = {res}-10530.342
{txt}Iteration 3:    log likelihood = {res}-10530.342


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-10530.342{txt}  
Iteration 1:{col 16}log likelihood = {res}-10530.342{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-10530.316{txt}  
Iteration 3:{col 16}log likelihood = {res}-10530.316{txt}  
{res} 
{txt}number of level 1 units = {res}11802
{txt}number of level 2 units = {res}1023
 
{txt}Condition Number = {res}102.88735
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-10530.316
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.2820641{col 26}{space 2}  .104469{col 37}{space 1}   -2.70{col 46}{space 3}0.007{col 54}{space 4}-.4868197{col 67}{space 3}-.0773086
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.236229{col 26}{space 2} .1211323{col 37}{space 1}   10.21{col 46}{space 3}0.000{col 54}{space 4} .9988141{col 67}{space 3} 1.473644
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.800707{col 26}{space 2} .2146284{col 37}{space 1}   17.71{col 46}{space 3}0.000{col 54}{space 4} 3.380043{col 67}{space 3} 4.221371
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.05482712 (.01232396)
{txt}    gw_know: {res}.06309679 (.03667631)
{txt}    pluralism: {res}.16452189 (.11575067)
{txt}    educ_plural: {res}-.1306921 (.03211809)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}1.0604984 (.12565274)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}.96804503 (.02350425)
{txt}    d3: {res}.64742961 (.02175452)
{txt}    d4: {res}1.3780469 (.03498496)
{txt}    d5: {res}1.0121159 (.02482664)
{txt}    d6: {res}1.0836982 (.02568738)
{txt}    d7: {res}1.4225419 (.03587317)
{txt}    d8: {res}.9439942 (.0234641)
{txt}    d9: {res}.79713491 (.02271296)
{txt}    d10: {res}1.31137 (.03241669)
{txt}    d11: {res}.55102879 (.02292921)
{txt}    d12: {res}1.0408964 (.02469598)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}4{txt} covariates:
    education: {res}.334433 (.03466081)
{txt}    ideo: {res}.08054591 (.01843183)
{txt}    pid: {res}.61297561 (.05787791)
{txt}    network: {res}.80976247 (.1208969)
{txt}------------------------------------------------------------------------------

{res} 

{com}. 
. Spearman pid nep 
{err}unrecognized command:  Spearman not defined by Spearman.ado
{txt}{search r(199):r(199);}

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. eq f1: race gender education ideo pid risk nep economy network  
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-10332.914
{txt}Iteration 1:    log likelihood = {res}-10212.018
{txt}Iteration 2:    log likelihood = {res}-9817.1439
{txt}Iteration 3:    log likelihood = {res}-9736.3636
{txt}Iteration 4:    log likelihood = {res}-9674.6909
{txt}Iteration 5:    log likelihood = {res}-9634.0608
{txt}Iteration 6:    log likelihood = {res}-9607.8427
{txt}Iteration 7:    log likelihood = {res}-9586.3215
{txt}Iteration 8:    log likelihood = {res}-9562.0756
{txt}Iteration 9:    log likelihood = {res}-9560.7822
{txt}Iteration 10:    log likelihood = {res}-9554.0438
{txt}Iteration 11:    log likelihood = {res}-9553.2144
{txt}Iteration 12:    log likelihood = {res}-9553.2144


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-9553.2144{txt}  (not concave)
Iteration 1:{col 16}log likelihood = {res}-9553.2144{txt}  
Iteration 2:{col 16}log likelihood = {res}-9551.7838{txt}  
Iteration 3:{col 16}log likelihood = {res}-9551.6731{txt}  (not concave)
Iteration 4:{col 16}log likelihood = {res}-9551.5873{txt}  
Iteration 5:{col 16}log likelihood = {res}-9551.5464{txt}  
Iteration 6:{col 16}log likelihood = {res} -9551.546{txt}  
{res} 
{txt}number of level 1 units = {res}11378
{txt}number of level 2 units = {res}984
 
{txt}Condition Number = {res}463.57982
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-9551.546
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut11       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} .7451238{col 26}{space 2} .2103919{col 37}{space 1}    3.54{col 46}{space 3}0.000{col 54}{space 4} .3327632{col 67}{space 3} 1.157484
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.011401{col 26}{space 2} .1732396{col 37}{space 1}    5.84{col 46}{space 3}0.000{col 54}{space 4} .6718578{col 67}{space 3} 1.350944
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} -.019886{col 26}{space 2}  .235272{col 37}{space 1}   -0.08{col 46}{space 3}0.933{col 54}{space 4}-.4810107{col 67}{space 3} .4412386
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .5376941{col 26}{space 2} .2776649{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4} -.006519{col 67}{space 3} 1.081907
{txt}{space 10}d6 {c |}{col 14}{res}{space 2}-.0756745{col 26}{space 2} .2402834{col 37}{space 1}   -0.31{col 46}{space 3}0.753{col 54}{space 4}-.5466213{col 67}{space 3} .3952722
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-1.027431{col 26}{space 2} .3308155{col 37}{space 1}   -3.11{col 46}{space 3}0.002{col 54}{space 4}-1.675817{col 67}{space 3}-.3790444
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .1801225{col 26}{space 2} .1980258{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4} -.208001{col 67}{space 3}  .568246
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .1077566{col 26}{space 2} .1487395{col 37}{space 1}    0.72{col 46}{space 3}0.469{col 54}{space 4}-.1837674{col 67}{space 3} .3992805
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.4063284{col 26}{space 2} .2601688{col 37}{space 1}   -1.56{col 46}{space 3}0.118{col 54}{space 4}-.9162498{col 67}{space 3} .1035931
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.139436{col 26}{space 2} .1377273{col 37}{space 1}    8.27{col 46}{space 3}0.000{col 54}{space 4} .8694958{col 67}{space 3} 1.409377
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .3783833{col 26}{space 2} .1696042{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0459653{col 67}{space 3} .7108014
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.579958{col 26}{space 2} .1477538{col 37}{space 1}  -10.69{col 46}{space 3}0.000{col 54}{space 4} -1.86955{col 67}{space 3}-1.290366
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut12       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.153658{col 26}{space 2} .1892265{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4}  .782781{col 67}{space 3} 1.524535
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.834038{col 26}{space 2}   .15785{col 37}{space 1}   11.62{col 46}{space 3}0.000{col 54}{space 4} 1.524657{col 67}{space 3} 2.143418
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0418832{col 26}{space 2} .1941869{col 37}{space 1}   -0.22{col 46}{space 3}0.829{col 54}{space 4}-.4224825{col 67}{space 3} .3387162
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.039848{col 26}{space 2} .2572165{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} .5357131{col 67}{space 3} 1.543983
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .6450943{col 26}{space 2}  .193107{col 37}{space 1}    3.34{col 46}{space 3}0.001{col 54}{space 4} .2666115{col 67}{space 3} 1.023577
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.5914339{col 26}{space 2} .1337979{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-.8536729{col 67}{space 3}-.3291949
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .7934978{col 26}{space 2}  .156744{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .4862851{col 67}{space 3}  1.10071
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5434471{col 26}{space 2} .1022826{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4}  .342977{col 67}{space 3} .7439173
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .2120229{col 26}{space 2} .1948313{col 37}{space 1}    1.09{col 46}{space 3}0.276{col 54}{space 4}-.1698394{col 67}{space 3} .5938851
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.620684{col 26}{space 2} .1165783{col 37}{space 1}   13.90{col 46}{space 3}0.000{col 54}{space 4} 1.392195{col 67}{space 3} 1.849174
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6987426{col 26}{space 2} .1347857{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4} .4345675{col 67}{space 3} .9629177
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6086964{col 26}{space 2} .1255192{col 37}{space 1}   -4.85{col 46}{space 3}0.000{col 54}{space 4}-.8547094{col 67}{space 3}-.3626833
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}_cut13       {txt}{c |}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.221477{col 26}{space 2} .2196085{col 37}{space 1}    5.56{col 46}{space 3}0.000{col 54}{space 4} .7910517{col 67}{space 3} 1.651901
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.516325{col 26}{space 2} .1928106{col 37}{space 1}    7.86{col 46}{space 3}0.000{col 54}{space 4} 1.138423{col 67}{space 3} 1.894226
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} .3385665{col 26}{space 2} .2109245{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4} -.074838{col 67}{space 3}  .751971
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.938841{col 26}{space 2} .3101754{col 37}{space 1}    6.25{col 46}{space 3}0.000{col 54}{space 4} 1.330909{col 67}{space 3} 2.546774
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} 1.148839{col 26}{space 2} .2264256{col 37}{space 1}    5.07{col 46}{space 3}0.000{col 54}{space 4} .7050531{col 67}{space 3} 1.592625
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.4191275{col 26}{space 2} .1346712{col 37}{space 1}   -3.11{col 46}{space 3}0.002{col 54}{space 4}-.6830781{col 67}{space 3}-.1551768
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.134072{col 26}{space 2} .1952418{col 37}{space 1}    5.81{col 46}{space 3}0.000{col 54}{space 4} .7514051{col 67}{space 3} 1.516739
{txt}{space 10}d9 {c |}{col 14}{res}{space 2}  .501351{col 26}{space 2} .1334454{col 37}{space 1}    3.76{col 46}{space 3}0.000{col 54}{space 4} .2398029{col 67}{space 3} .7628992
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .5417039{col 26}{space 2} .2157661{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .1188101{col 67}{space 3} .9645977
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} .9078175{col 26}{space 2} .1464685{col 37}{space 1}    6.20{col 46}{space 3}0.000{col 54}{space 4} .6207446{col 67}{space 3} 1.194891
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .4517607{col 26}{space 2} .1576645{col 37}{space 1}    2.87{col 46}{space 3}0.004{col 54}{space 4} .1427439{col 67}{space 3} .7607775
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.574598{col 26}{space 2} .1347447{col 37}{space 1}   11.69{col 46}{space 3}0.000{col 54}{space 4} 1.310504{col 67}{space 3} 1.838693
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.1072603 (.02159983)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.5295697 (.27779064)
{txt}    d3: {res}2.088234 (.23405364)
{txt}    d4: {res}2.5532512 (.28606785)
{txt}    d5: {res}3.2559219 (.37597302)
{txt}    d6: {res}2.5792664 (.29251266)
{txt}    d7: {res}1.5861506 (.18981495)
{txt}    d8: {res}2.1206698 (.24386436)
{txt}    d9: {res}1.0524326 (.14106715)
{txt}    d10: {res}2.5797491 (.2891368)
{txt}    d11: {res}1.4668737 (.17347234)
{txt}    d12: {res}1.8631197 (.21071888)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}-.013581 (.03339627)
{txt}    gender: {res}-.02390226 (.02450198)
{txt}    education: {res}.02949598 (.01209366)
{txt}    ideo: {res}-.02609743 (.00897299)
{txt}    pid: {res}.02985584 (.01929115)
{txt}    risk: {res}.56194324 (.085746)
{txt}    nep: {res}.75916943 (.1116172)
{txt}    economy: {res}-.42697754 (.07069667)
{txt}    network: {res}.09020164 (.04107333)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,57]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}   .7451238   1.0114011  -.01988604   .53769412  -.07567455  -1.0274309   .18012249   .10775656  -.40632835

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.1394364   .37838334   -1.579958    1.153658   1.8340377  -.04188316   1.0398481   .64509433  -.59143389

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}   .7934978   .54344714   .21202285   1.6206844    .6987426  -.60869636   1.2214765   1.5163246   .33856648

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  1.9388412   1.1488392  -.41912746   1.1340719   .50135104   .54170386   .90781755   .45176068   1.5745983

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  2.5295697    2.088234   2.5532512   3.2559219   2.5792664   1.5861506   2.1206698   1.0524326   2.5797491

{txt}        id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:         f1:         f1:         f1:
           d11         d12          d1        race      gender   education        ideo         pid        risk
y1 {res}  1.4668737   1.8631197   .32750618    -.013581  -.02390226   .02949598  -.02609743   .02985584   .56194324

{txt}            f1:         f1:         f1:
           nep     economy     network
y1 {res}  .75916943  -.42697754   .09020164
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. eq het: education gw_know pluralism educ_plural pid pid_plural
{txt}
{com}. 
{txt}end of do-file

{com}. clear

. clear matrix

. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. set more off, permanently
{txt}({cmd:set more} preference recorded)

{com}. 
. ** increase memory
. set mem 10g
{txt}
{title:Current memory allocation}

{col 21}current{col 61}memory usage
{col 5}settable{col 23}value{col 33}description{col 61}(1M = 1024k)
{col 5}{hline 68}
{col 5}set maxvar{col 22}{res:  5000}{col 33}max. variables allowed           2.105M
{col 5}set memory        {res:10240M}{col 33}max. data space             10,240.000M
{col 5}set matsize{col 21}{res:    400}{col 33}max. RHS vars in models          1.254M
{col 61}{hline 11}
{col 55}      10,243.359M

{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. use "/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/noaasammy.dta"
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. ********************************************************
. ///* DEMOGRAPHIC VARIABLES AND POLITICAL ORIENTATION*///
> ********************************************************
. ** generate id variable 
. gen id = _n
{txt}
{com}. 
. /*RACE*/
. /*white = 1 non-white = 0 */
. /*Lose 37 cases */
. rename q119fin1 race
{txt}
{com}. recode race 1=0 2=1 3=0 4=0 5=0 100=0 101=0 102=0 103=0
{txt}(race: 1056 changes made)

{com}. label drop q119fin1
{txt}
{com}. label define q119fin1 0 "non-white" 1 "white"
{txt}
{com}. tab race

       {txt}race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  non-white {c |}{res}        168       15.91       15.91
{txt}      white {c |}{res}        888       84.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,056      100.00
{txt}
{com}. 
. /*EDUCATION*/
. /*Less than high school = 0 Post-Graduate = 1*/
. rename q116 education
{txt}
{com}. recode education 3=2 4=3 5=4 6=5
{txt}(education: 838 changes made)

{com}. label drop q116
{txt}
{com}. label define q116 1 "some high school"2 "high school/vocational" 3 "some college"  4 "college" 5 "post-graduate" 
{txt}
{com}. codebook education 

{txt}{hline}
{res}education{right:education}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q116}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}12{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}     22{col 33}       1{col 43}{txt}some high school
{col 24}{res}    241{col 33}       2{col 43}{txt}high school/vocational
{col 24}{res}    303{col 33}       3{col 43}{txt}some college
{col 24}{res}    331{col 33}       4{col 43}{txt}college
{col 24}{res}    184{col 33}       5{col 43}{txt}post-graduate
{col 24}{res}     12{col 33}       .{col 43}
{txt}
{com}. 
. /*INCOME*/
. /*Lose 244 cases*/
. rename q122 income
{txt}
{com}. 
. /*AGE*/
. /*Ranges from 18 to 90*/
. /*Lose 32 cases*/
. rename q117 age
{txt}
{com}. 
. /*RELIGIOUS ATTENDANCE*/
. /* Attendend = 1 Not attend = 0*/
. /* lose 16 cases*/
. rename q124 attendance
{txt}
{com}. recode attendance 2=0
{txt}(attendance: 0 changes made)

{com}. 
. /*IDEOLOGY*/
. /* Lose 134 cases*/
. rename q118 ideo
{txt}
{com}. 
. /*PARTISANSHIP*/
. /* Lose 46 cases*/
. rename q114 pid
{txt}
{com}. 
. ** recode democrat = 1 republican = -1
. recode pid 2 = 0 3 = 1 1 = 2 8 = 1 9 = 1 
{txt}(pid: 1093 changes made)

{com}. label drop q114
{txt}
{com}. label define q114 0 "republican" 1 "independent/else" 2 "democrat"  
{txt}
{com}. codebook pid 

{txt}{hline}
{res}pid{right:suppose you were in the voting booth and you came across an office for which two}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q114}

{col 18}range:  [{res}0{txt},{res}2{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}0{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    256{col 33}       0{col 43}{txt}republican
{col 24}{res}    480{col 33}       1{col 43}{txt}independent/else
{col 24}{res}    357{col 33}       2{col 43}{txt}democrat

{com}. 
. /*EFFICACY*/
. /*Higher values are associated with lower efficacy*/
. /*Lose 102 cases*/
. cor q71 q73 q74
{txt}(obs=991)

             {c |}      q71      q73      q74
{hline 13}{c +}{hline 27}
         q71 {c |}{res}   1.0000
         {txt}q73 {c |}{res}   0.3621   1.0000
         {txt}q74 {c |}{res}   0.4882   0.2457   1.0000

{txt}
{com}. factor q71 q73 q74
{txt}(obs=991)

Factor analysis/correlation{col 52}Number of obs    = {res}     991
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.98296      1.04220            1.4417       1.4417
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.05924      0.18268           -0.0869       1.3548
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.24192            .           -0.3548       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  415.39{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q71}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6594}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5652}}}{space 1}
{space 4}{space 0}{ralign 12:q73}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4526}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7952}}}{space 1}
{space 4}{space 0}{ralign 12:q74}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5859}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6567}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q71 q73 q74, detail gen (efficacy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .1693972
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6338

{txt}Interitem covariances (obs=pairwise, see below)

        q71     q73     q74
q71  {res}0.5078
{txt}q73  {res}0.1624  0.4029
{txt}q74  {res}0.2380  0.1055  0.4767

{txt}Pairwise number of observations

      q71   q73   q74
q71  {res}1073
{txt}q73  {res}1023  1035
{txt}q74  {res}1033   999  1045
{txt}
{com}. 
. /*RISK PERCEPTIONS*/
. /*Higher values are associated with higher risk perception */ 
. recode q101 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q101: 1037 changes made)

{com}. recode q102 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q102: 1014 changes made)

{com}. recode q103 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q103: 1031 changes made)

{com}. factor q101 q102 q103 
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.77400      1.89251            1.1834       1.1834
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.11851      0.03796           -0.0791       1.1044
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.15648            .           -0.1044       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1191.31{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7924}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3720}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7273}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4710}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7855}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3829}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q101 q102 q103, detail gen (risk)  

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .040741
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8428

{txt}Interitem covariances (obs=pairwise, see below)

        q101    q102    q103
q101  {res}0.0722
{txt}q102  {res}0.0407  0.0592
{txt}q103  {res}0.0450  0.0364  0.0591

{txt}Pairwise number of observations

      q101  q102  q103
q101  {res}1037
{txt}q102  {res} 998  1014
{txt}q103  {res}1007   993  1031
{txt}
{com}. 
. /*NETWORK INTEREST*/
. /*Higher values associated with greater network interest*/
. recode q81 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q81: 1087 changes made)

{com}. recode q82 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q82: 1087 changes made)

{com}. factor q81 q82 q83 q85
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.81814      1.53358            1.0636       1.0636
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.28456      0.47109            0.1665       1.2301
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.18653      0.02025           -0.1091       1.1210
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.20678            .           -0.1210       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1382.63{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7265}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2610}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4041}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7721}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2041}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3622}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6111}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2818}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5472}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5664}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3089}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5838}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q81 q82 q83 q85, detail gen (network)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0729956
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7462

{txt}Interitem covariances (obs=pairwise, see below)

        q81     q82     q83     q85
q81  {res}0.1045
{txt}q82  {res}0.0727  0.1010
{txt}q83  {res}0.0594  0.0667  0.2449
{txt}q85  {res}0.0501  0.0593  0.1296  0.2390

{txt}Pairwise number of observations

      q81   q82   q83   q85
q81  {res}1087
{txt}q82  {res}1086  1087
{txt}q83  {res}1086  1086  1089
{txt}q85  {res}1081  1081  1084  1084
{txt}
{com}. 
. 
. ** create ideological strength 
. gen strength_ideo = 0 
{txt}
{com}. recode strength_ideo 0 = 3 if ideo == 1
{txt}(strength_ideo: 58 changes made)

{com}. recode strength_ideo 0 = 3 if ideo == 7
{txt}(strength_ideo: 119 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 6
{txt}(strength_ideo: 180 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 2
{txt}(strength_ideo: 179 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 3
{txt}(strength_ideo: 108 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 5
{txt}(strength_ideo: 145 changes made)

{com}. 
. 
. ************************************
. ///* INDICATORS OF INFORMATION *///
> ************************************
. /* SCIENTIFIC INFORMATION 1 */
. /* 1 correct, 0 wrong */
. /* Lose 13 cases */
. cor q12 q13 q14 q15
{txt}(obs=1080)

             {c |}      q12      q13      q14      q15
{hline 13}{c +}{hline 36}
         q12 {c |}{res}   1.0000
         {txt}q13 {c |}{res}   0.0521   1.0000
         {txt}q14 {c |}{res}   0.2260   0.0802   1.0000
         {txt}q15 {c |}{res}   0.2196   0.0005   0.1004   1.0000

{txt}
{com}. factor q12 q13 q14 q15 
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.45391      0.42155            2.1711       2.1711
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.03236      0.11624            0.1548       2.3259
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08388      0.10945           -0.4012       1.9247
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.19332            .           -0.9247       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  121.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q12}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4447}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0189}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8019}}}{space 1}
{space 4}{space 0}{ralign 12:q13}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1176}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1449}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9652}}}{space 1}
{space 4}{space 0}{ralign 12:q14}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3625}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0571}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8653}}}{space 1}
{space 4}{space 0}{ralign 12:q15}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3330}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0881}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8814}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q12 q13 q14 q15, detail gen (sci_obknowledge)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240184
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.3552

{txt}Interitem covariances (obs=pairwise, see below)

        q12     q13     q14     q15
q12  {res}0.2477
{txt}q13  {res}0.0096  0.1255
{txt}q14  {res}0.0473  0.0116  0.1730
{txt}q15  {res}0.0543  0.0005  0.0208  0.2477

{txt}Pairwise number of observations

      q12   q13   q14   q15
q12  {res}1088
{txt}q13  {res}1086  1088
{txt}q14  {res}1087  1087  1089
{txt}q15  {res}1082  1082  1084  1085
{txt}
{com}. 
. /* Domain-Specific Knowldge of GW "causes" */
. cor q62 q63 q66 q67
{txt}(obs=1085)

             {c |}      q62      q63      q66      q67
{hline 13}{c +}{hline 36}
         q62 {c |}{res}   1.0000
         {txt}q63 {c |}{res}   0.1959   1.0000
         {txt}q66 {c |}{res}   0.1202   0.0779   1.0000
         {txt}q67 {c |}{res}   0.0701  -0.0175   0.0895   1.0000

{txt}
{com}. factor q62 q63 q66 q67  
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.33173      0.27333            2.4835       2.4835
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.05840      0.14591            0.4372       2.9207
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08752      0.08152           -0.6552       2.2655
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16904            .           -1.2655       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}   75.58{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0304}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8591}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3171}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1234}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8842}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2675}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0928}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9198}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1400}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9468}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q62 q63 q66 q67,detail gen (gw_know)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201437
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2859

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2407
{txt}q63  {res} 0.0436   0.2077
{txt}q66  {res} 0.0293   0.0172   0.2465
{txt}q67  {res} 0.0149  -0.0034   0.0193   0.1905

{txt}Pairwise number of observations

      q62   q63   q66   q67
q62  {res}1088
{txt}q63  {res}1088  1089
{txt}q66  {res}1085  1086  1086
{txt}q67  {res}1086  1087  1086  1087
{txt}
{com}. 
. /* Domain-specific by partisans and ideology */ 
. alpha q62 q63 q66 q67 if pid==0, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201991
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2817

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2334
{txt}q63  {res} 0.0440   0.2301
{txt}q66  {res} 0.0134   0.0399   0.2419
{txt}q67  {res} 0.0141  -0.0145   0.0242   0.1996

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}253
{txt}q63  {res}253  253
{txt}q66  {res}252  252  252
{txt}q67  {res}252  252  252  252
{txt}
{com}. alpha q62 q63 q66 q67 if pid==2, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0186036
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2793

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2434
{txt}q63  {res}0.0349  0.1789
{txt}q66  {res}0.0375  0.0057  0.2507
{txt}q67  {res}0.0143  0.0025  0.0167  0.1696

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}357
{txt}q63  {res}357  357
{txt}q66  {res}356  356  356
{txt}q67  {res}357  357  356  357
{txt}
{com}. 
. alpha q62 q63 q66 q67 if ideo==1, detail

{txt}Test scale = mean(unstandardized items)
Reversed item: {res: q63}

Average interitem covariance:{col 34}{res}  .015729
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2470

{txt}Interitem covariances (reverse applied) (obs=58 in all pairs)

         q62      q63      q66      q67
q62  {res} 0.2396
{txt}q63  {res}-0.0163   0.1770
{txt}q66  {res} 0.0333   0.0284   0.2468
{txt}q67  {res} 0.0079   0.0230   0.0181   0.1670
{txt}
{com}. alpha q62 q63 q66 q67 if ideo==7, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0343651
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.4037

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2241
{txt}q63  {res}0.0546  0.2495
{txt}q66  {res}0.0074  0.0527  0.2490
{txt}q67  {res}0.0309  0.0067  0.0536  0.2269

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}117
{txt}q63  {res}117  118
{txt}q66  {res}116  117  117
{txt}q67  {res}116  117  117  117
{txt}
{com}. 
. 
. ** Descriptive analysis  
. ** Correlation between pid ideo and gw_know education 
. spearman gw_know education

{txt} Number of obs = {res}   1077
{txt}Spearman's rho = {res}     -0.0036

{txt}Test of Ho: gw_know and education are independent
    Prob > |t| = {res}      0.9070
{txt}
{com}. spearman gw_know ideo

{txt} Number of obs = {res}   1044
{txt}Spearman's rho = {res}     -0.0695

{txt}Test of Ho: gw_know and ideo are independent
    Prob > |t| = {res}      0.0247
{txt}
{com}. spearman gw_know pid

{txt} Number of obs = {res}   1089
{txt}Spearman's rho = {res}      0.0650

{txt}Test of Ho: gw_know and pid are independent
    Prob > |t| = {res}      0.0320
{txt}
{com}. spearman ideo pid

{txt} Number of obs = {res}   1047
{txt}Spearman's rho = {res}     -0.5449

{txt}Test of Ho: ideo and pid are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman gw_know sci_obknowledge

{txt} Number of obs = {res}   1088
{txt}Spearman's rho = {res}      0.1123

{txt}Test of Ho: gw_know and sci_obknowledge are independent
    Prob > |t| = {res}      0.0002
{txt}
{com}. 
. *********************************************
. ///* 1 - |NEP - HEP| CORE VALUE CONFLICT *///
> *********************************************
. /*Recode HEP and NEP components to 0 to 1 scale with higher values indicating pro-enviornmental/human paradigm*/
. recode q20 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q20: 1063 changes made)

{com}. recode q21 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q21: 1057 changes made)

{com}. recode q22 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q22: 1064 changes made)

{com}. recode q23 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q23: 1026 changes made)

{com}. recode q24 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q24: 1079 changes made)

{com}. recode q25 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q25: 1069 changes made)

{com}. recode q26 4 = 0 3 = .33 2 = .67
{txt}(q26: 1020 changes made)

{com}. recode q27 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q27: 1040 changes made)

{com}. recode q28 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q28: 1044 changes made)

{com}. recode q29 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q29: 1064 changes made)

{com}. recode q30 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q30: 1053 changes made)

{com}. recode q31 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q31: 1073 changes made)

{com}. recode q32 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q32: 1046 changes made)

{com}. recode q33 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q33: 1040 changes made)

{com}. 
. /*NEP*/
. /* Range 1 (high environmental concern) to 0 (environmental concern) */
. alpha q20 q22 q24 q29 q31 q33, detail gen (nep)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0202498
{txt}Number of items in the scale:{col 34}{res}        6
{txt}Scale reliability coefficient:{col 34}{res}   0.7804

{txt}Interitem covariances (obs=pairwise, see below)

        q20     q22     q24     q29     q31     q33
q20  {res}0.0745
{txt}q22  {res}0.0187  0.0540
{txt}q24  {res}0.0218  0.0249  0.0527
{txt}q29  {res}0.0243  0.0120  0.0145  0.0474
{txt}q31  {res}0.0172  0.0206  0.0207  0.0159  0.0437
{txt}q33  {res}0.0241  0.0229  0.0285  0.0162  0.0216  0.0545

{txt}Pairwise number of observations

      q20   q22   q24   q29   q31   q33
q20  {res}1063
{txt}q22  {res}1042  1064
{txt}q24  {res}1051  1052  1079
{txt}q29  {res}1041  1041  1052  1064
{txt}q31  {res}1048  1051  1061  1050  1073
{txt}q33  {res}1019  1023  1029  1016  1028  1040
{txt}
{com}. 
. ********************************************************
. ///* 1 - |NEP - ECONOMIC PRIORITIES| VALUE CONFLICT *///
> ********************************************************
. /* Higher values indicate greater support for economy*/
. recode q7 4=1 3=.67 2=.33 1=0
{txt}(q7: 1051 changes made)

{com}. recode q9 4=0 3=.33 2=.67 
{txt}(q9: 948 changes made)

{com}. alpha q7 q9, detail gen(economy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0282268
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.5409

{txt}Interitem covariances (obs=pairwise, see below)

        q7      q9
q7  {res}0.0722
{txt}q9  {res}0.0282  0.0801

{txt}Pairwise number of observations

      q7    q9
q7  {res}1051
{txt}q9  {res}1021  1052
{txt}
{com}. gen nepvecon = 1 - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. 
. ** Pluralism and interactions
. ** create value pluralism 
. gen pluralism = ((nep + economy)/2) - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. ** create interaction of education and value pluralism
. gen educ_plural = education * pluralism 
{txt}(23 missing values generated)

{com}. 
. ** create interaction of global warming knowledge and value pluralism 
. gen  gwknow_plural = gw_know * pluralism 
{txt}(15 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_plural = pid * pluralism 
{txt}(12 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_gw_pluralism = pid * gwknow_plural
{txt}(15 missing values generated)

{com}. 
. 
. ***************************************************
. ///* CLIMATE CHANGE POLICY DEPENDENT VARIABLES *///
> ***************************************************
. /* In this area we create the dependent variables.  These are based upon policy preferences  */
. /* with regard to solving CC problems.  They are on a four-point scale */
. 
. /* First, combine q93_v1 and q93_v2, as they are conceptually the same, but differently worded.  This will */
. /* allow us to have a variable comparable in number of observations to the others */
. 
. recode q93_v1 .=0
{txt}(q93_v1: 595 changes made)

{com}. recode q93_v2 .=0
{txt}(q93_v2: 528 changes made)

{com}. gen q93 = q93_v1 + q93_v2
{txt}
{com}. /* Recode 0 in q93 to missing value so that it is not included */
. recode q93 0=.
{txt}(q93: 30 changes made)

{com}. 
. /* Second, rename the variables of policy preferences */
. 
. rename q89 emission
{txt}
{com}. rename q90 tax_industry
{txt}
{com}. rename q91 tax_individuals
{txt}
{com}. rename q92 educatepublic
{txt}
{com}. rename q93 setprice
{txt}
{com}. rename q94 kyoto
{txt}
{com}. rename q95 law
{txt}
{com}. rename q96 renewable
{txt}
{com}. rename q97 methane
{txt}
{com}. rename q98 seawalls
{txt}
{com}. rename q99 vehicle
{txt}
{com}. rename q100 gas
{txt}
{com}. 
. ** correlation of items with partisanship 
. spearman pid emission

{txt} Number of obs = {res}   1036
{txt}Spearman's rho = {res}      0.0585

{txt}Test of Ho: pid and emission are independent
    Prob > |t| = {res}      0.0600
{txt}
{com}. spearman pid tax_industry

{txt} Number of obs = {res}   1049
{txt}Spearman's rho = {res}      0.2262

{txt}Test of Ho: pid and tax_industry are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid tax_individuals

{txt} Number of obs = {res}   1038
{txt}Spearman's rho = {res}      0.2205

{txt}Test of Ho: pid and tax_individuals are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid educatepublic

{txt} Number of obs = {res}   1074
{txt}Spearman's rho = {res}      0.1534

{txt}Test of Ho: pid and educatepublic are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid setprice

{txt} Number of obs = {res}   1063
{txt}Spearman's rho = {res}      0.1836

{txt}Test of Ho: pid and setprice are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid kyoto

{txt} Number of obs = {res}    978
{txt}Spearman's rho = {res}      0.3177

{txt}Test of Ho: pid and kyoto are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid law

{txt} Number of obs = {res}   1058
{txt}Spearman's rho = {res}      0.2237

{txt}Test of Ho: pid and law are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid renewable

{txt} Number of obs = {res}   1061
{txt}Spearman's rho = {res}      0.0905

{txt}Test of Ho: pid and renewable are independent
    Prob > |t| = {res}      0.0032
{txt}
{com}. spearman pid methane

{txt} Number of obs = {res}    972
{txt}Spearman's rho = {res}      0.2198

{txt}Test of Ho: pid and methane are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid seawalls

{txt} Number of obs = {res}    971
{txt}Spearman's rho = {res}      0.1152

{txt}Test of Ho: pid and seawalls are independent
    Prob > |t| = {res}      0.0003
{txt}
{com}. spearman pid vehicle

{txt} Number of obs = {res}   1073
{txt}Spearman's rho = {res}      0.1377

{txt}Test of Ho: pid and vehicle are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid gas

{txt} Number of obs = {res}   1058
{txt}Spearman's rho = {res}      0.2626

{txt}Test of Ho: pid and gas are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. 
{txt}end of do-file

{com}. tab pluralism 

  {txt}pluralism {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        -.5 {c |}{res}          6        0.56        0.56
{txt}     -.4725 {c |}{res}          5        0.46        1.02
{txt}      -.445 {c |}{res}          7        0.65        1.67
{txt}  -.4441667 {c |}{res}          1        0.09        1.76
{txt}     -.4175 {c |}{res}          8        0.74        2.50
{txt}  -.4166667 {c |}{res}          2        0.19        2.68
{txt}      -.401 {c |}{res}          1        0.09        2.78
{txt}       -.39 {c |}{res}          3        0.28        3.05
{txt}  -.3891667 {c |}{res}          2        0.19        3.24
{txt}      -.368 {c |}{res}          1        0.09        3.33
{txt}     -.3625 {c |}{res}          3        0.28        3.61
{txt}     -.3625 {c |}{res}          1        0.09        3.70
{txt}  -.3616667 {c |}{res}          2        0.19        3.89
{txt}  -.3608333 {c |}{res}          2        0.19        4.07
{txt}      -.335 {c |}{res}         12        1.11        5.18
{txt}      -.335 {c |}{res}          2        0.19        5.37
{txt}  -.3341667 {c |}{res}          6        0.56        5.92
{txt}  -.3333333 {c |}{res}          1        0.09        6.01
{txt}     -.3325 {c |}{res}          1        0.09        6.11
{txt}  -.3066667 {c |}{res}          5        0.46        6.57
{txt}  -.3066667 {c |}{res}          1        0.09        6.66
{txt}  -.3058333 {c |}{res}          1        0.09        6.75
{txt}      -.301 {c |}{res}          2        0.19        6.94
{txt}        -.3 {c |}{res}          1        0.09        7.03
{txt}  -.2783333 {c |}{res}          4        0.37        7.40
{txt}  -.2766667 {c |}{res}          1        0.09        7.49
{txt}  -.2766666 {c |}{res}          1        0.09        7.59
{txt}      -.266 {c |}{res}          1        0.09        7.68
{txt}     -.2525 {c |}{res}          2        0.19        7.86
{txt}  -.2508334 {c |}{res}          2        0.19        8.05
{txt}       -.25 {c |}{res}          2        0.19        8.23
{txt}      -.225 {c |}{res}          3        0.28        8.51
{txt}     -.2225 {c |}{res}          1        0.09        8.60
{txt}  -.2216667 {c |}{res}          1        0.09        8.70
{txt}     -.2195 {c |}{res}          1        0.09        8.79
{txt}      -.201 {c |}{res}          1        0.09        8.88
{txt}     -.1975 {c |}{res}          4        0.37        9.25
{txt}      -.195 {c |}{res}          1        0.09        9.34
{txt}     -.1865 {c |}{res}          1        0.09        9.44
{txt}       -.17 {c |}{res}         12        1.11       10.55
{txt}       -.17 {c |}{res}          1        0.09       10.64
{txt}       -.17 {c |}{res}          1        0.09       10.73
{txt}  -.1691667 {c |}{res}          1        0.09       10.82
{txt}      -.165 {c |}{res}          1        0.09       10.92
{txt}     -.1425 {c |}{res}          5        0.46       11.38
{txt}  -.1416667 {c |}{res}          4        0.37       11.75
{txt}  -.1408333 {c |}{res}          2        0.19       11.93
{txt}  -.1383333 {c |}{res}          1        0.09       12.03
{txt}      -.115 {c |}{res}          1        0.09       12.12
{txt}      -.115 {c |}{res}          1        0.09       12.21
{txt}  -.1141667 {c |}{res}          8        0.74       12.95
{txt}  -.1133333 {c |}{res}          1        0.09       13.04
{txt}     -.0875 {c |}{res}          7        0.65       13.69
{txt}     -.0875 {c |}{res}          1        0.09       13.78
{txt}  -.0866667 {c |}{res}         10        0.93       14.71
{txt}     -.0865 {c |}{res}          1        0.09       14.80
{txt}  -.0858333 {c |}{res}          1        0.09       14.89
{txt}      -.085 {c |}{res}          3        0.28       15.17
{txt}     -.0825 {c |}{res}          1        0.09       15.26
{txt}  -.0591667 {c |}{res}          3        0.28       15.54
{txt}  -.0591667 {c |}{res}          6        0.56       16.10
{txt}  -.0583333 {c |}{res}          4        0.37       16.47
{txt}     -.0535 {c |}{res}          1        0.09       16.56
{txt}     -.0525 {c |}{res}          1        0.09       16.65
{txt}  -.0316667 {c |}{res}          1        0.09       16.74
{txt}  -.0308333 {c |}{res}         13        1.20       17.95
{txt}       -.03 {c |}{res}          1        0.09       18.04
{txt}     -.0215 {c |}{res}          1        0.09       18.13
{txt}      -.005 {c |}{res}          2        0.19       18.32
{txt}      -.005 {c |}{res}          1        0.09       18.41
{txt}      -.005 {c |}{res}          2        0.19       18.59
{txt}  -.0033334 {c |}{res}          1        0.09       18.69
{txt}     -.0025 {c |}{res}          3        0.28       18.96
{txt}     -.0025 {c |}{res}          6        0.56       19.52
{txt}  -.0016667 {c |}{res}          1        0.09       19.61
{txt}  -1.49e-08 {c |}{res}          1        0.09       19.70
{txt}   3.73e-09 {c |}{res}          1        0.09       19.80
{txt}      .0025 {c |}{res}          1        0.09       19.89
{txt}      .0225 {c |}{res}          5        0.46       20.35
{txt}       .025 {c |}{res}          3        0.28       20.63
{txt}   .0258333 {c |}{res}          1        0.09       20.72
{txt}   .0258334 {c |}{res}          2        0.19       20.91
{txt}       .028 {c |}{res}          1        0.09       21.00
{txt}        .03 {c |}{res}          1        0.09       21.09
{txt}        .05 {c |}{res}          8        0.74       21.83
{txt}   .0508334 {c |}{res}          2        0.19       22.02
{txt}   .0533333 {c |}{res}          1        0.09       22.11
{txt}      .0575 {c |}{res}          1        0.09       22.20
{txt}      .0695 {c |}{res}          1        0.09       22.29
{txt}      .0775 {c |}{res}         15        1.39       23.68
{txt}        .08 {c |}{res}          2        0.19       23.87
{txt}        .08 {c |}{res}          1        0.09       23.96
{txt}   .0816667 {c |}{res}          1        0.09       24.05
{txt}      .0825 {c |}{res}          1        0.09       24.14
{txt}   .0858334 {c |}{res}          1        0.09       24.24
{txt}       .094 {c |}{res}          1        0.09       24.33
{txt}       .097 {c |}{res}          1        0.09       24.42
{txt}       .105 {c |}{res}         15        1.39       25.81
{txt}   .1058333 {c |}{res}          4        0.37       26.18
{txt}      .1125 {c |}{res}          1        0.09       26.27
{txt}       .127 {c |}{res}          1        0.09       26.36
{txt}       .128 {c |}{res}          1        0.09       26.46
{txt}      .1325 {c |}{res}         15        1.39       27.84
{txt}      .1325 {c |}{res}          2        0.19       28.03
{txt}   .1333333 {c |}{res}         11        1.02       29.05
{txt}   .1341667 {c |}{res}          3        0.28       29.32
{txt}   .1408333 {c |}{res}          1        0.09       29.42
{txt}        .16 {c |}{res}         40        3.70       33.12
{txt}   .1608333 {c |}{res}         11        1.02       34.14
{txt}       .161 {c |}{res}          1        0.09       34.23
{txt}   .1616667 {c |}{res}          6        0.56       34.78
{txt}      .1625 {c |}{res}          7        0.65       35.43
{txt}   .1649999 {c |}{res}          1        0.09       35.52
{txt}       .165 {c |}{res}          4        0.37       35.89
{txt}      .1675 {c |}{res}          3        0.28       36.17
{txt}   .1683333 {c |}{res}          1        0.09       36.26
{txt}   .1691667 {c |}{res}          2        0.19       36.45
{txt}   .1883333 {c |}{res}         23        2.13       38.58
{txt}   .1883333 {c |}{res}         22        2.04       40.61
{txt}   .1891667 {c |}{res}         11        1.02       41.63
{txt}        .19 {c |}{res}          1        0.09       41.72
{txt}       .194 {c |}{res}          2        0.19       41.91
{txt}       .194 {c |}{res}          2        0.19       42.09
{txt}   .1958333 {c |}{res}          1        0.09       42.18
{txt}   .1966667 {c |}{res}          1        0.09       42.28
{txt}      .1975 {c |}{res}          1        0.09       42.37
{txt}      .2025 {c |}{res}          3        0.28       42.65
{txt}   .2158334 {c |}{res}          1        0.09       42.74
{txt}   .2166667 {c |}{res}         40        3.70       46.44
{txt}      .2175 {c |}{res}          6        0.56       46.99
{txt}       .228 {c |}{res}          2        0.19       47.18
{txt}      .2355 {c |}{res}          1        0.09       47.27
{txt}   .2441667 {c |}{res}          1        0.09       47.36
{txt}   .2441667 {c |}{res}          1        0.09       47.46
{txt}       .245 {c |}{res}          4        0.37       47.83
{txt}       .245 {c |}{res}          1        0.09       47.92
{txt}       .245 {c |}{res}         30        2.78       50.69
{txt}       .245 {c |}{res}          1        0.09       50.79
{txt}   .2458333 {c |}{res}          1        0.09       50.88
{txt}   .2458334 {c |}{res}          1        0.09       50.97
{txt}   .2474999 {c |}{res}          3        0.28       51.25
{txt}      .2475 {c |}{res}          3        0.28       51.53
{txt}      .2475 {c |}{res}          4        0.37       51.90
{txt}      .2475 {c |}{res}          5        0.46       52.36
{txt}       .248 {c |}{res}          1        0.09       52.45
{txt}        .25 {c |}{res}          1        0.09       52.54
{txt}        .25 {c |}{res}          4        0.37       52.91
{txt}   .2516667 {c |}{res}          2        0.19       53.10
{txt}      .2525 {c |}{res}          1        0.09       53.19
{txt}   .2533334 {c |}{res}          1        0.09       53.28
{txt}       .262 {c |}{res}          1        0.09       53.38
{txt}      .2645 {c |}{res}          1        0.09       53.47
{txt}      .2675 {c |}{res}          1        0.09       53.56
{txt}      .2725 {c |}{res}          2        0.19       53.75
{txt}   .2733333 {c |}{res}         11        1.02       54.76
{txt}   .2733334 {c |}{res}          4        0.37       55.13
{txt}   .2741667 {c |}{res}          1        0.09       55.23
{txt}      .2775 {c |}{res}          1        0.09       55.32
{txt}      .2815 {c |}{res}          2        0.19       55.50
{txt}      .2815 {c |}{res}          1        0.09       55.60
{txt}      .2875 {c |}{res}          2        0.19       55.78
{txt}       .295 {c |}{res}          1        0.09       55.87
{txt}       .296 {c |}{res}          1        0.09       55.97
{txt}         .3 {c |}{res}          1        0.09       56.06
{txt}   .3016667 {c |}{res}         10        0.93       56.98
{txt}       .305 {c |}{res}         12        1.11       58.09
{txt}   .3083333 {c |}{res}          1        0.09       58.19
{txt}       .329 {c |}{res}          1        0.09       58.28
{txt}   .3291667 {c |}{res}          2        0.19       58.46
{txt}   .3299999 {c |}{res}          5        0.46       58.93
{txt}        .33 {c |}{res}          1        0.09       59.02
{txt}        .33 {c |}{res}          5        0.46       59.48
{txt}        .33 {c |}{res}         19        1.76       61.24
{txt}   .3324999 {c |}{res}          1        0.09       61.33
{txt}      .3325 {c |}{res}         12        1.11       62.44
{txt}      .3325 {c |}{res}          1        0.09       62.53
{txt}      .3325 {c |}{res}          5        0.46       63.00
{txt}   .3325001 {c |}{res}          1        0.09       63.09
{txt}       .335 {c |}{res}          2        0.19       63.27
{txt}       .335 {c |}{res}          1        0.09       63.37
{txt}   .3470001 {c |}{res}          3        0.28       63.64
{txt}       .349 {c |}{res}          1        0.09       63.74
{txt}        .36 {c |}{res}          9        0.83       64.57
{txt}   .3608333 {c |}{res}          3        0.28       64.85
{txt}       .364 {c |}{res}          2        0.19       65.03
{txt}       .364 {c |}{res}          1        0.09       65.12
{txt}      .3725 {c |}{res}          1        0.09       65.22
{txt}       .382 {c |}{res}          1        0.09       65.31
{txt}       .383 {c |}{res}          1        0.09       65.40
{txt}      .3875 {c |}{res}         11        1.02       66.42
{txt}   .3883333 {c |}{res}          7        0.65       67.07
{txt}       .415 {c |}{res}          4        0.37       67.44
{txt}       .415 {c |}{res}         46        4.26       71.69
{txt}       .415 {c |}{res}         10        0.93       72.62
{txt}   .4150001 {c |}{res}          1        0.09       72.71
{txt}   .4158333 {c |}{res}          9        0.83       73.54
{txt}       .416 {c |}{res}          2        0.19       73.73
{txt}   .4166667 {c |}{res}          4        0.37       74.10
{txt}      .4175 {c |}{res}          3        0.28       74.38
{txt}      .4175 {c |}{res}          6        0.56       74.93
{txt}        .42 {c |}{res}          1        0.09       75.02
{txt}   .4433333 {c |}{res}         20        1.85       76.87
{txt}   .4433333 {c |}{res}         15        1.39       78.26
{txt}   .4441667 {c |}{res}          8        0.74       79.00
{txt}       .445 {c |}{res}          1        0.09       79.09
{txt}       .449 {c |}{res}          3        0.28       79.37
{txt}       .449 {c |}{res}          6        0.56       79.93
{txt}       .452 {c |}{res}          1        0.09       80.02
{txt}      .4575 {c |}{res}          1        0.09       80.11
{txt}   .4660001 {c |}{res}          2        0.19       80.30
{txt}   .4708333 {c |}{res}          1        0.09       80.39
{txt}   .4716667 {c |}{res}         40        3.70       84.09
{txt}      .4725 {c |}{res}          4        0.37       84.46
{txt}   .4733334 {c |}{res}          1        0.09       84.55
{txt}       .483 {c |}{res}          4        0.37       84.92
{txt}       .483 {c |}{res}          1        0.09       85.01
{txt}   .4974999 {c |}{res}          1        0.09       85.11
{txt}   .4974999 {c |}{res}          1        0.09       85.20
{txt}      .4975 {c |}{res}          4        0.37       85.57
{txt}   .4991666 {c |}{res}          1        0.09       85.66
{txt}         .5 {c |}{res}          3        0.28       85.94
{txt}         .5 {c |}{res}         38        3.52       89.45
{txt}   .5000001 {c |}{res}          1        0.09       89.55
{txt}   .5020001 {c |}{res}          1        0.09       89.64
{txt}      .5025 {c |}{res}          6        0.56       90.19
{txt}   .5025001 {c |}{res}          2        0.19       90.38
{txt}       .505 {c |}{res}          2        0.19       90.56
{txt}       .525 {c |}{res}          2        0.19       90.75
{txt}      .5325 {c |}{res}          1        0.09       90.84
{txt}      .5525 {c |}{res}          1        0.09       90.93
{txt}        .56 {c |}{res}          1        0.09       91.03
{txt}   .5680001 {c |}{res}          1        0.09       91.12
{txt}        .58 {c |}{res}          3        0.28       91.40
{txt}   .5824999 {c |}{res}          1        0.09       91.49
{txt}      .5825 {c |}{res}          6        0.56       92.04
{txt}   .5845001 {c |}{res}          1        0.09       92.14
{txt}       .585 {c |}{res}         19        1.76       93.89
{txt}   .5850001 {c |}{res}          8        0.74       94.63
{txt}      .5875 {c |}{res}          5        0.46       95.10
{txt}   .5875001 {c |}{res}          3        0.28       95.37
{txt}   .6091667 {c |}{res}          1        0.09       95.47
{txt}       .615 {c |}{res}          4        0.37       95.84
{txt}     .62875 {c |}{res}          1        0.09       95.93
{txt}       .635 {c |}{res}          1        0.09       96.02
{txt}   .6366667 {c |}{res}          2        0.19       96.21
{txt}   .6433333 {c |}{res}          1        0.09       96.30
{txt}      .6625 {c |}{res}          3        0.28       96.58
{txt}   .6641667 {c |}{res}          3        0.28       96.85
{txt}       .665 {c |}{res}          2        0.19       97.04
{txt}      .6675 {c |}{res}          4        0.37       97.41
{txt}        .67 {c |}{res}         17        1.57       98.98
{txt}   .6700001 {c |}{res}          1        0.09       99.07
{txt}   .6865001 {c |}{res}          1        0.09       99.17
{txt}        .75 {c |}{res}          1        0.09       99.26
{txt}   .7800001 {c |}{res}          1        0.09       99.35
{txt}      .8075 {c |}{res}          2        0.19       99.54
{txt}   .8083334 {c |}{res}          1        0.09       99.63
{txt}      .8325 {c |}{res}          2        0.19       99.81
{txt}       .835 {c |}{res}          2        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,081      100.00

{com}. tab radio
{err}variable radio not found
{txt}{search r(111):r(111);}

{com}. tab q78_6

 {txt}information {c |}
source use - {c |}
    research {c |}
conferences  {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
       never {c |}{res}        719       67.20       67.20
{txt}          1| {c |}{res}         59        5.51       72.71
{txt}          2| {c |}{res}         60        5.61       78.32
{txt}          3| {c |}{res}         46        4.30       82.62
{txt}          4| {c |}{res}         37        3.46       86.07
{txt}          5| {c |}{res}         65        6.07       92.15
{txt}          6| {c |}{res}         23        2.15       94.30
{txt}          7| {c |}{res}         11        1.03       95.33
{txt}          8| {c |}{res}         21        1.96       97.29
{txt}          9| {c |}{res}          9        0.84       98.13
{txt}  very often {c |}{res}         20        1.87      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,070      100.00

{com}. drop if q78_6 == 4 
{txt}(37 observations deleted)

{com}. tab q115

         {txt}vote for {c |}
 president counts {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
strongly disagree {c |}{res}         40        3.79        3.79
{txt}         disagree {c |}{res}        102        9.66       13.45
{txt}            agree {c |}{res}        432       40.91       54.36
{txt}   strongly agree {c |}{res}        464       43.94       98.30
{txt}     [don't know] {c |}{res}         13        1.23       99.53
{txt}        [refused] {c |}{res}          5        0.47      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,056      100.00

{com}. sort id

. drop if id > 1000
{txt}(88 observations deleted)

{com}. drop if id < 50
{txt}(49 observations deleted)

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. /****************
>     GLLAMM 
> ****************/
. 
. ** rename response/outcome variables
. rename emission item_1
{txt}
{com}. rename tax_industry item_2
{txt}
{com}. rename tax_individuals item_3
{txt}
{com}. rename educatepublic item_4
{txt}
{com}. rename kyoto item_5
{txt}
{com}. rename law item_6 
{txt}
{com}. rename renewable item_7
{txt}
{com}. rename methane item_8
{txt}
{com}. rename seawalls item_9
{txt}
{com}. rename vehicle item_10
{txt}
{com}. rename gas item_11
{txt}
{com}. rename setprice item_12
{txt}
{com}. 
. ** change data into long form 
. reshape long item_, i(id) j(item)
{txt}(note: j = 1 2 3 4 5 6 7 8 9 10 11 12)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}     919   {txt}->{res}   11028
{txt}Number of variables            {res}     264   {txt}->{res}     254
{txt}j variable (12 values)                    ->   {res}item
{txt}xij variables:
              {res}item_1 item_2 ... item_12   {txt}->   {res}item_
{txt}{hline 77}

{com}. rename item_ y
{txt}
{com}. qui tab item, gen(d)
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. *******************
. **ESTIMATION 
. ********************
. ** model specification 
. eq load: d1-d12
{txt}
{com}. eq thr: d2-d12
{txt}
{com}. eq f1: race gender education ideo pid risk nep economy network  
{txt}
{com}. 
. ** estimate structural model (no het) 
. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-8710.7715
{txt}Iteration 1:    log likelihood = {res} -8490.296
{txt}Iteration 2:    log likelihood = {res}-8190.7492
{txt}Iteration 3:    log likelihood = {res}-8122.0811
{txt}Iteration 4:    log likelihood = {res}-8112.4551
{txt}Iteration 5:    log likelihood = {res}-8089.7946
{txt}Iteration 6:    log likelihood = {res}-8089.7946


{txt}Adaptive quadrature has converged, running Newton-Raphson
{err}(Maximization aborted)
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,57]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  1.4182699   1.5674685   .50830432   1.2364903   .69991341  -.23788034   .86599681   .61415697   .41766112

{txt}        _cut11:     _cut11:     _cut11:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.7152196   .88665571  -1.8446363   1.2548536    1.916346   .15375679   1.2435302   .72874943  -.72866316

{txt}        _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut12:     _cut13:     _cut13:     _cut13:
            d8          d9         d10         d11         d12       _cons          d2          d3          d4
y1 {res}  .88891783   .53629832   .36854048   1.7336128   .70722385  -.38782833   .91657965   1.2802588   .12057292

{txt}        _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:     _cut13:
            d5          d6          d7          d8          d9         d10         d11         d12       _cons
y1 {res}  2.0269156   .87166523  -.87097038    .8936874   .21606715   .24686372   .70069828   .04972487   2.1769816

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  1.6302573   1.4114701   1.6765287   2.3235939   1.6647546   .95617054   1.4507981   .80168364   1.6555371

{txt}        id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:         f1:         f1:         f1:
           d11         d12          d1        race      gender   education        ideo         pid        risk
y1 {res}  1.1027357   1.1873875   .48409887   .01233086  -.04367706   .05884439  -.02629787    .0559057   .85950971

{txt}            f1:         f1:         f1:
           nep     economy     network
y1 {res}  1.2174844  -.57338411   .11212233
{reset}
{com}. 
. 
{txt}end of do-file

{com}. clear

. clear matrix

. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. set more off, permanently
{txt}({cmd:set more} preference recorded)

{com}. 
. ** increase memory
. set mem 10g
{txt}
{title:Current memory allocation}

{col 21}current{col 61}memory usage
{col 5}settable{col 23}value{col 33}description{col 61}(1M = 1024k)
{col 5}{hline 68}
{col 5}set maxvar{col 22}{res:  5000}{col 33}max. variables allowed           2.105M
{col 5}set memory        {res:10240M}{col 33}max. data space             10,240.000M
{col 5}set matsize{col 21}{res:    400}{col 33}max. RHS vars in models          1.254M
{col 61}{hline 11}
{col 55}      10,243.359M

{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. use "/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/noaasammy.dta"
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. ********************************************************
. ///* DEMOGRAPHIC VARIABLES AND POLITICAL ORIENTATION*///
> ********************************************************
. ** generate id variable 
. gen id = _n
{txt}
{com}. 
. /*RACE*/
. /*white = 1 non-white = 0 */
. /*Lose 37 cases */
. rename q119fin1 race
{txt}
{com}. recode race 1=0 2=1 3=0 4=0 5=0 100=0 101=0 102=0 103=0
{txt}(race: 1056 changes made)

{com}. label drop q119fin1
{txt}
{com}. label define q119fin1 0 "non-white" 1 "white"
{txt}
{com}. tab race

       {txt}race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  non-white {c |}{res}        168       15.91       15.91
{txt}      white {c |}{res}        888       84.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,056      100.00
{txt}
{com}. 
. /*EDUCATION*/
. /*Less than high school = 0 Post-Graduate = 1*/
. rename q116 education
{txt}
{com}. recode education 3=2 4=3 5=4 6=5
{txt}(education: 838 changes made)

{com}. label drop q116
{txt}
{com}. label define q116 1 "some high school"2 "high school/vocational" 3 "some college"  4 "college" 5 "post-graduate" 
{txt}
{com}. codebook education 

{txt}{hline}
{res}education{right:education}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q116}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}12{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}     22{col 33}       1{col 43}{txt}some high school
{col 24}{res}    241{col 33}       2{col 43}{txt}high school/vocational
{col 24}{res}    303{col 33}       3{col 43}{txt}some college
{col 24}{res}    331{col 33}       4{col 43}{txt}college
{col 24}{res}    184{col 33}       5{col 43}{txt}post-graduate
{col 24}{res}     12{col 33}       .{col 43}
{txt}
{com}. 
. /*INCOME*/
. /*Lose 244 cases*/
. rename q122 income
{txt}
{com}. 
. /*AGE*/
. /*Ranges from 18 to 90*/
. /*Lose 32 cases*/
. rename q117 age
{txt}
{com}. 
. /*RELIGIOUS ATTENDANCE*/
. /* Attendend = 1 Not attend = 0*/
. /* lose 16 cases*/
. rename q124 attendance
{txt}
{com}. recode attendance 2=0
{txt}(attendance: 0 changes made)

{com}. 
. /*IDEOLOGY*/
. /* Lose 134 cases*/
. rename q118 ideo
{txt}
{com}. 
. /*PARTISANSHIP*/
. /* Lose 46 cases*/
. rename q114 pid
{txt}
{com}. 
. ** recode democrat = 1 republican = -1
. recode pid 2 = 0 3 = 1 1 = 2 8 = 1 9 = 1 
{txt}(pid: 1093 changes made)

{com}. label drop q114
{txt}
{com}. label define q114 0 "republican" 1 "independent/else" 2 "democrat"  
{txt}
{com}. codebook pid 

{txt}{hline}
{res}pid{right:suppose you were in the voting booth and you came across an office for which two}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q114}

{col 18}range:  [{res}0{txt},{res}2{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}0{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    256{col 33}       0{col 43}{txt}republican
{col 24}{res}    480{col 33}       1{col 43}{txt}independent/else
{col 24}{res}    357{col 33}       2{col 43}{txt}democrat

{com}. 
. /*EFFICACY*/
. /*Higher values are associated with lower efficacy*/
. /*Lose 102 cases*/
. cor q71 q73 q74
{txt}(obs=991)

             {c |}      q71      q73      q74
{hline 13}{c +}{hline 27}
         q71 {c |}{res}   1.0000
         {txt}q73 {c |}{res}   0.3621   1.0000
         {txt}q74 {c |}{res}   0.4882   0.2457   1.0000

{txt}
{com}. factor q71 q73 q74
{txt}(obs=991)

Factor analysis/correlation{col 52}Number of obs    = {res}     991
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.98296      1.04220            1.4417       1.4417
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.05924      0.18268           -0.0869       1.3548
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.24192            .           -0.3548       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  415.39{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q71}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6594}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5652}}}{space 1}
{space 4}{space 0}{ralign 12:q73}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4526}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7952}}}{space 1}
{space 4}{space 0}{ralign 12:q74}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5859}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6567}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q71 q73 q74, detail gen (efficacy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .1693972
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6338

{txt}Interitem covariances (obs=pairwise, see below)

        q71     q73     q74
q71  {res}0.5078
{txt}q73  {res}0.1624  0.4029
{txt}q74  {res}0.2380  0.1055  0.4767

{txt}Pairwise number of observations

      q71   q73   q74
q71  {res}1073
{txt}q73  {res}1023  1035
{txt}q74  {res}1033   999  1045
{txt}
{com}. 
. /*RISK PERCEPTIONS*/
. /*Higher values are associated with higher risk perception */ 
. recode q101 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q101: 1037 changes made)

{com}. recode q102 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q102: 1014 changes made)

{com}. recode q103 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q103: 1031 changes made)

{com}. factor q101 q102 q103 
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.77400      1.89251            1.1834       1.1834
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.11851      0.03796           -0.0791       1.1044
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.15648            .           -0.1044       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1191.31{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7924}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3720}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7273}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4710}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7855}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3829}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q101 q102 q103, detail gen (risk)  

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .040741
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8428

{txt}Interitem covariances (obs=pairwise, see below)

        q101    q102    q103
q101  {res}0.0722
{txt}q102  {res}0.0407  0.0592
{txt}q103  {res}0.0450  0.0364  0.0591

{txt}Pairwise number of observations

      q101  q102  q103
q101  {res}1037
{txt}q102  {res} 998  1014
{txt}q103  {res}1007   993  1031
{txt}
{com}. 
. /*NETWORK INTEREST*/
. /*Higher values associated with greater network interest*/
. recode q81 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q81: 1087 changes made)

{com}. recode q82 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q82: 1087 changes made)

{com}. factor q81 q82 q83 q85
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.81814      1.53358            1.0636       1.0636
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.28456      0.47109            0.1665       1.2301
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.18653      0.02025           -0.1091       1.1210
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.20678            .           -0.1210       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1382.63{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7265}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2610}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4041}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7721}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2041}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3622}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6111}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2818}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5472}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5664}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3089}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5838}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q81 q82 q83 q85, detail gen (network)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0729956
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7462

{txt}Interitem covariances (obs=pairwise, see below)

        q81     q82     q83     q85
q81  {res}0.1045
{txt}q82  {res}0.0727  0.1010
{txt}q83  {res}0.0594  0.0667  0.2449
{txt}q85  {res}0.0501  0.0593  0.1296  0.2390

{txt}Pairwise number of observations

      q81   q82   q83   q85
q81  {res}1087
{txt}q82  {res}1086  1087
{txt}q83  {res}1086  1086  1089
{txt}q85  {res}1081  1081  1084  1084
{txt}
{com}. 
. 
. ** create ideological strength 
. gen strength_ideo = 0 
{txt}
{com}. recode strength_ideo 0 = 3 if ideo == 1
{txt}(strength_ideo: 58 changes made)

{com}. recode strength_ideo 0 = 3 if ideo == 7
{txt}(strength_ideo: 119 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 6
{txt}(strength_ideo: 180 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 2
{txt}(strength_ideo: 179 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 3
{txt}(strength_ideo: 108 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 5
{txt}(strength_ideo: 145 changes made)

{com}. 
. 
. ************************************
. ///* INDICATORS OF INFORMATION *///
> ************************************
. /* SCIENTIFIC INFORMATION 1 */
. /* 1 correct, 0 wrong */
. /* Lose 13 cases */
. cor q12 q13 q14 q15
{txt}(obs=1080)

             {c |}      q12      q13      q14      q15
{hline 13}{c +}{hline 36}
         q12 {c |}{res}   1.0000
         {txt}q13 {c |}{res}   0.0521   1.0000
         {txt}q14 {c |}{res}   0.2260   0.0802   1.0000
         {txt}q15 {c |}{res}   0.2196   0.0005   0.1004   1.0000

{txt}
{com}. factor q12 q13 q14 q15 
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.45391      0.42155            2.1711       2.1711
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.03236      0.11624            0.1548       2.3259
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08388      0.10945           -0.4012       1.9247
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.19332            .           -0.9247       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  121.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q12}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4447}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0189}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8019}}}{space 1}
{space 4}{space 0}{ralign 12:q13}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1176}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1449}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9652}}}{space 1}
{space 4}{space 0}{ralign 12:q14}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3625}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0571}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8653}}}{space 1}
{space 4}{space 0}{ralign 12:q15}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3330}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0881}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8814}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q12 q13 q14 q15, detail gen (sci_obknowledge)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240184
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.3552

{txt}Interitem covariances (obs=pairwise, see below)

        q12     q13     q14     q15
q12  {res}0.2477
{txt}q13  {res}0.0096  0.1255
{txt}q14  {res}0.0473  0.0116  0.1730
{txt}q15  {res}0.0543  0.0005  0.0208  0.2477

{txt}Pairwise number of observations

      q12   q13   q14   q15
q12  {res}1088
{txt}q13  {res}1086  1088
{txt}q14  {res}1087  1087  1089
{txt}q15  {res}1082  1082  1084  1085
{txt}
{com}. 
. /* Domain-Specific Knowldge of GW "causes" */
. cor q62 q63 q66 q67
{txt}(obs=1085)

             {c |}      q62      q63      q66      q67
{hline 13}{c +}{hline 36}
         q62 {c |}{res}   1.0000
         {txt}q63 {c |}{res}   0.1959   1.0000
         {txt}q66 {c |}{res}   0.1202   0.0779   1.0000
         {txt}q67 {c |}{res}   0.0701  -0.0175   0.0895   1.0000

{txt}
{com}. factor q62 q63 q66 q67  
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.33173      0.27333            2.4835       2.4835
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.05840      0.14591            0.4372       2.9207
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08752      0.08152           -0.6552       2.2655
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16904            .           -1.2655       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}   75.58{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0304}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8591}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3171}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1234}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8842}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2675}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0928}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9198}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1400}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9468}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q62 q63 q66 q67,detail gen (gw_know)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201437
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2859

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2407
{txt}q63  {res} 0.0436   0.2077
{txt}q66  {res} 0.0293   0.0172   0.2465
{txt}q67  {res} 0.0149  -0.0034   0.0193   0.1905

{txt}Pairwise number of observations

      q62   q63   q66   q67
q62  {res}1088
{txt}q63  {res}1088  1089
{txt}q66  {res}1085  1086  1086
{txt}q67  {res}1086  1087  1086  1087
{txt}
{com}. 
. /* Domain-specific by partisans and ideology */ 
. alpha q62 q63 q66 q67 if pid==0, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201991
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2817

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2334
{txt}q63  {res} 0.0440   0.2301
{txt}q66  {res} 0.0134   0.0399   0.2419
{txt}q67  {res} 0.0141  -0.0145   0.0242   0.1996

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}253
{txt}q63  {res}253  253
{txt}q66  {res}252  252  252
{txt}q67  {res}252  252  252  252
{txt}
{com}. alpha q62 q63 q66 q67 if pid==2, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0186036
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2793

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2434
{txt}q63  {res}0.0349  0.1789
{txt}q66  {res}0.0375  0.0057  0.2507
{txt}q67  {res}0.0143  0.0025  0.0167  0.1696

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}357
{txt}q63  {res}357  357
{txt}q66  {res}356  356  356
{txt}q67  {res}357  357  356  357
{txt}
{com}. 
. alpha q62 q63 q66 q67 if ideo==1, detail

{txt}Test scale = mean(unstandardized items)
Reversed item: {res: q63}

Average interitem covariance:{col 34}{res}  .015729
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2470

{txt}Interitem covariances (reverse applied) (obs=58 in all pairs)

         q62      q63      q66      q67
q62  {res} 0.2396
{txt}q63  {res}-0.0163   0.1770
{txt}q66  {res} 0.0333   0.0284   0.2468
{txt}q67  {res} 0.0079   0.0230   0.0181   0.1670
{txt}
{com}. alpha q62 q63 q66 q67 if ideo==7, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0343651
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.4037

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2241
{txt}q63  {res}0.0546  0.2495
{txt}q66  {res}0.0074  0.0527  0.2490
{txt}q67  {res}0.0309  0.0067  0.0536  0.2269

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}117
{txt}q63  {res}117  118
{txt}q66  {res}116  117  117
{txt}q67  {res}116  117  117  117
{txt}
{com}. 
. 
. ** Descriptive analysis  
. ** Correlation between pid ideo and gw_know education 
. spearman gw_know education

{txt} Number of obs = {res}   1077
{txt}Spearman's rho = {res}     -0.0036

{txt}Test of Ho: gw_know and education are independent
    Prob > |t| = {res}      0.9070
{txt}
{com}. spearman gw_know ideo

{txt} Number of obs = {res}   1044
{txt}Spearman's rho = {res}     -0.0695

{txt}Test of Ho: gw_know and ideo are independent
    Prob > |t| = {res}      0.0247
{txt}
{com}. spearman gw_know pid

{txt} Number of obs = {res}   1089
{txt}Spearman's rho = {res}      0.0650

{txt}Test of Ho: gw_know and pid are independent
    Prob > |t| = {res}      0.0320
{txt}
{com}. spearman ideo pid

{txt} Number of obs = {res}   1047
{txt}Spearman's rho = {res}     -0.5449

{txt}Test of Ho: ideo and pid are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman gw_know sci_obknowledge

{txt} Number of obs = {res}   1088
{txt}Spearman's rho = {res}      0.1123

{txt}Test of Ho: gw_know and sci_obknowledge are independent
    Prob > |t| = {res}      0.0002
{txt}
{com}. 
. *********************************************
. ///* 1 - |NEP - HEP| CORE VALUE CONFLICT *///
> *********************************************
. /*Recode HEP and NEP components to 0 to 1 scale with higher values indicating pro-enviornmental/human paradigm*/
. recode q20 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q20: 1063 changes made)

{com}. recode q21 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q21: 1057 changes made)

{com}. recode q22 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q22: 1064 changes made)

{com}. recode q23 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q23: 1026 changes made)

{com}. recode q24 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q24: 1079 changes made)

{com}. recode q25 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q25: 1069 changes made)

{com}. recode q26 4 = 0 3 = .33 2 = .67
{txt}(q26: 1020 changes made)

{com}. recode q27 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q27: 1040 changes made)

{com}. recode q28 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q28: 1044 changes made)

{com}. recode q29 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q29: 1064 changes made)

{com}. recode q30 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q30: 1053 changes made)

{com}. recode q31 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q31: 1073 changes made)

{com}. recode q32 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q32: 1046 changes made)

{com}. recode q33 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q33: 1040 changes made)

{com}. 
. /*NEP*/
. /* Range 1 (high environmental concern) to 0 (environmental concern) */
. alpha q20 q22 q24 q29 q31 q33, detail gen (nep)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0202498
{txt}Number of items in the scale:{col 34}{res}        6
{txt}Scale reliability coefficient:{col 34}{res}   0.7804

{txt}Interitem covariances (obs=pairwise, see below)

        q20     q22     q24     q29     q31     q33
q20  {res}0.0745
{txt}q22  {res}0.0187  0.0540
{txt}q24  {res}0.0218  0.0249  0.0527
{txt}q29  {res}0.0243  0.0120  0.0145  0.0474
{txt}q31  {res}0.0172  0.0206  0.0207  0.0159  0.0437
{txt}q33  {res}0.0241  0.0229  0.0285  0.0162  0.0216  0.0545

{txt}Pairwise number of observations

      q20   q22   q24   q29   q31   q33
q20  {res}1063
{txt}q22  {res}1042  1064
{txt}q24  {res}1051  1052  1079
{txt}q29  {res}1041  1041  1052  1064
{txt}q31  {res}1048  1051  1061  1050  1073
{txt}q33  {res}1019  1023  1029  1016  1028  1040
{txt}
{com}. 
. ********************************************************
. ///* 1 - |NEP - ECONOMIC PRIORITIES| VALUE CONFLICT *///
> ********************************************************
. /* Higher values indicate greater support for economy*/
. recode q7 4=1 3=.67 2=.33 1=0
{txt}(q7: 1051 changes made)

{com}. recode q9 4=0 3=.33 2=.67 
{txt}(q9: 948 changes made)

{com}. alpha q7 q9, detail gen(economy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0282268
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.5409

{txt}Interitem covariances (obs=pairwise, see below)

        q7      q9
q7  {res}0.0722
{txt}q9  {res}0.0282  0.0801

{txt}Pairwise number of observations

      q7    q9
q7  {res}1051
{txt}q9  {res}1021  1052
{txt}
{com}. gen nepvecon = 1 - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. 
. ** Pluralism and interactions
. ** create value pluralism 
. gen pluralism = ((nep + economy)/2) - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. ** create interaction of education and value pluralism
. gen educ_plural = education * pluralism 
{txt}(23 missing values generated)

{com}. 
. ** create interaction of global warming knowledge and value pluralism 
. gen  gwknow_plural = gw_know * pluralism 
{txt}(15 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_plural = pid * pluralism 
{txt}(12 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_gw_pluralism = pid * gwknow_plural
{txt}(15 missing values generated)

{com}. 
. 
. ***************************************************
. ///* CLIMATE CHANGE POLICY DEPENDENT VARIABLES *///
> ***************************************************
. /* In this area we create the dependent variables.  These are based upon policy preferences  */
. /* with regard to solving CC problems.  They are on a four-point scale */
. 
. /* First, combine q93_v1 and q93_v2, as they are conceptually the same, but differently worded.  This will */
. /* allow us to have a variable comparable in number of observations to the others */
. 
. recode q93_v1 .=0
{txt}(q93_v1: 595 changes made)

{com}. recode q93_v2 .=0
{txt}(q93_v2: 528 changes made)

{com}. gen q93 = q93_v1 + q93_v2
{txt}
{com}. /* Recode 0 in q93 to missing value so that it is not included */
. recode q93 0=.
{txt}(q93: 30 changes made)

{com}. 
. /* Second, rename the variables of policy preferences */
. 
. rename q89 emission
{txt}
{com}. rename q90 tax_industry
{txt}
{com}. rename q91 tax_individuals
{txt}
{com}. rename q92 educatepublic
{txt}
{com}. rename q93 setprice
{txt}
{com}. rename q94 kyoto
{txt}
{com}. rename q95 law
{txt}
{com}. rename q96 renewable
{txt}
{com}. rename q97 methane
{txt}
{com}. rename q98 seawalls
{txt}
{com}. rename q99 vehicle
{txt}
{com}. rename q100 gas
{txt}
{com}. 
. ** correlation of items with partisanship 
. spearman pid emission

{txt} Number of obs = {res}   1036
{txt}Spearman's rho = {res}      0.0585

{txt}Test of Ho: pid and emission are independent
    Prob > |t| = {res}      0.0600
{txt}
{com}. spearman pid tax_industry

{txt} Number of obs = {res}   1049
{txt}Spearman's rho = {res}      0.2262

{txt}Test of Ho: pid and tax_industry are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid tax_individuals

{txt} Number of obs = {res}   1038
{txt}Spearman's rho = {res}      0.2205

{txt}Test of Ho: pid and tax_individuals are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid educatepublic

{txt} Number of obs = {res}   1074
{txt}Spearman's rho = {res}      0.1534

{txt}Test of Ho: pid and educatepublic are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid setprice

{txt} Number of obs = {res}   1063
{txt}Spearman's rho = {res}      0.1836

{txt}Test of Ho: pid and setprice are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid kyoto

{txt} Number of obs = {res}    978
{txt}Spearman's rho = {res}      0.3177

{txt}Test of Ho: pid and kyoto are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid law

{txt} Number of obs = {res}   1058
{txt}Spearman's rho = {res}      0.2237

{txt}Test of Ho: pid and law are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid renewable

{txt} Number of obs = {res}   1061
{txt}Spearman's rho = {res}      0.0905

{txt}Test of Ho: pid and renewable are independent
    Prob > |t| = {res}      0.0032
{txt}
{com}. spearman pid methane

{txt} Number of obs = {res}    972
{txt}Spearman's rho = {res}      0.2198

{txt}Test of Ho: pid and methane are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid seawalls

{txt} Number of obs = {res}    971
{txt}Spearman's rho = {res}      0.1152

{txt}Test of Ho: pid and seawalls are independent
    Prob > |t| = {res}      0.0003
{txt}
{com}. spearman pid vehicle

{txt} Number of obs = {res}   1073
{txt}Spearman's rho = {res}      0.1377

{txt}Test of Ho: pid and vehicle are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman pid gas

{txt} Number of obs = {res}   1058
{txt}Spearman's rho = {res}      0.2626

{txt}Test of Ho: pid and gas are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. 
. /****************
>     GLLAMM 
> ****************/
. 
. ** rename response/outcome variables
. rename emission item_1
{txt}
{com}. rename tax_industry item_2
{txt}
{com}. rename tax_individuals item_3
{txt}
{com}. rename educatepublic item_4
{txt}
{com}. rename kyoto item_5
{txt}
{com}. rename law item_6 
{txt}
{com}. rename renewable item_7
{txt}
{com}. rename methane item_8
{txt}
{com}. rename seawalls item_9
{txt}
{com}. rename vehicle item_10
{txt}
{com}. rename gas item_11
{txt}
{com}. rename setprice item_12
{txt}
{com}. 
. ** change data into long form 
. reshape long item_, i(id) j(item)
{txt}(note: j = 1 2 3 4 5 6 7 8 9 10 11 12)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1093   {txt}->{res}   13116
{txt}Number of variables            {res}     264   {txt}->{res}     254
{txt}j variable (12 values)                    ->   {res}item
{txt}xij variables:
              {res}item_1 item_2 ... item_12   {txt}->   {res}item_
{txt}{hline 77}

{com}. rename item_ y
{txt}
{com}. qui tab item, gen(d)
{txt}
{com}. 
. 
. *******************
. **ESTIMATION 
. ********************
. ** model specification 
. eq load: d1-d12
{txt}
{com}. eq thr: d2-d12
{txt}
{com}. eq f1: race gender education ideo pid risk nep economy network  
{txt}
{com}. 
. ** estimate structural model (no het) 
. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-10332.914
{txt}Iteration 1:    log likelihood = {res}-10212.018
{txt}Iteration 2:    log likelihood = {res}-9817.1439
{txt}Iteration 3:    log likelihood = {res}-9736.3635
{txt}Iteration 4:    log likelihood = {res}-9674.6901
{txt}Iteration 5:    log likelihood = {res}-9634.0603
{txt}Iteration 6:    log likelihood = {res}-9626.4219
{txt}Iteration 7:    log likelihood = {res}-9588.1926
